Total 5,183 Others data entry job vacancies

• Manage Lockout/Tagout (LOTO) procedures and ensure compliance across all contractors.
• Act as the primary liaison between construction, commissioning, and operations teams regarding energy safety.
• Review and approve energy isolation plans and permits.
• Conduct field inspections and audits to ensure adherence to safety protocols.
• Stop unsafe work practices and enforce OSHA and NFPA 70E standards.
• Monitor and report on energy usage and performance metrics.
• Validate mechanical and electrical system performance during commissioning.
• Assist in implementing energy-saving strategies and sustainability best practices.
• Maintain detailed records of energy control activities and compliance metrics.
• Train and mentor teams on electrical safety and energy isolation procedures.

Responsibilities

• Develop and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
• Build and support batch and real-time data processing solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Monitor, troubleshoot, and optimize data pipelines and workflows.
• Collaborate with business, analytics, and technology teams to deliver data-driven solutions.
• Support cloud-based data platforms and data warehousing initiatives.
• Develop automation and monitoring solutions to improve operational efficiency.
• Any other ad-hoc duties as assigned by supervisor.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 3+ years of Data Engineering or ETL development experience.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Hadoop, Spark, Kafka, or similar big data technologies.
• Knowledge of data warehousing and data modeling concepts.
• Experience with cloud platforms such as Azure Data Factory, Azure Synapse, Databricks, or Azure Data Lake.
• Strong problem-solving and stakeholder management skills.

Technical Skills

• Experience with Apache Flink or real-time streaming technologies.
• Exposure to Power BI, Tableau, Qlik, or other BI tools.
• Familiarity with AI/LLM frameworks such as LangChain, LangGraph, or RAG solutions.
• Experience within banking, financial services, or large enterprise environments.
• Tech Stack: Python, PySpark, SQL, Hadoop, Spark, Kafka, Azure, Databricks, Power BI/Tableau.

Interested applicants, please email your resume to Andre Chua Jing Ming

Email: andrechua@recruitexpress.com.sg

CEI Reg No: R1989053

EA Licence No: 99C4599

Recruit Express Pte Ltd

Responsibilities

• Develop and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
• Build and support batch and real-time data processing solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Monitor, troubleshoot, and optimize data pipelines and workflows.
• Collaborate with business, analytics, and technology teams to deliver data-driven solutions.
• Support cloud-based data platforms and data warehousing initiatives.
• Develop automation and monitoring solutions to improve operational efficiency.
• Any other ad-hoc duties as assigned by supervisor.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 3+ years of Data Engineering or ETL development experience.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Hadoop, Spark, Kafka, or similar big data technologies.
• Knowledge of data warehousing and data modeling concepts.
• Experience with cloud platforms such as Azure Data Factory, Azure Synapse, Databricks, or Azure Data Lake.
• Strong problem-solving and stakeholder management skills.

Technical Skills

• Experience with Apache Flink or real-time streaming technologies.
• Exposure to Power BI, Tableau, Qlik, or other BI tools.
• Familiarity with AI/LLM frameworks such as LangChain, LangGraph, or RAG solutions.
• Experience within banking, financial services, or large enterprise environments.
• Tech Stack: Python, PySpark, SQL, Hadoop, Spark, Kafka, Azure, Databricks, Power BI/Tableau.

Interested applicants, please email your resume to Andre Chua Jing Ming

Email: andrechua@recruitexpress.com.sg

CEI Reg No: R1989053

EA Licence No: 99C4599

Recruit Express Pte Ltd

Contributes to the development of new analytics tool, software, and approaches, including the design, development, troubleshooting and debugging.

  *   Ongoing maintenance, update and support of the existing programming and analytic tools

  *   Develop, implement and maintain server and database infrastructure, hardware and software to ensure maximum tool uptime and performance.

  *   Execute end-to-end data processing workflows, ensuring data is accurately ingested, cleaned and structured across corporate systems.

  *   Manage and monitor data flows between current and future infrastructure components to maintain data integrity and consistency.

  *   Analyze processed data to uncover trends, patterns and operational performance metrics using statistical techniques.

  *   Exercises significant independent judgment to determine best method for accomplishing work and achieving objectives

  *   Leads and /or provides expertise to functional project teams and may participate in cross-functional initiatives.

The requirements:

  *   5 years of hands-on experience in data engineering and analytics

  *   Strong skills in statistical tools and programming languages such as Python, R, etc.

  *   Experience with data analysis / visualization tools and software like Power BI, Looker, Databricks etc.

  *   Proficiency in SQL for data extraction and manipulation.

  *   Attention to detail and a commitment to data accuracy.

  *   Proactive approach to identifying issues and providing solutions.

  *   Strong analytical and problem-solving skills. Ability to analyze complex datasets and translate data into actionable insights.

  *   Ability to present complex information in an understandable and compelling manner.

  *   Excellent written and verbal communication skills

  *   Ability to work independently and collaboratively as part of team

•You will be responsible for end-to-end delivery of enterprise data and analytics solutions leveraging traditional and modern Data architecture. Experience with Financial Crime Analytics, Finance & Credit Risk Analytics, Credit Scoring & Decision systems, Retail & Wholesale Datamarts will of advantage

•The role spans requirements analysis, solution design, testing, implementation, and production support ensuring high-quality, scalable, and compliant data platforms that support advanced analytics and AI initiatives.

Key Responsibilities

•Lead end-to-end solution delivery for data and analytics across the full SDLC.

•Analyze business and regulatory requirements, translate them into scalable solution designs & provide estimations.

•Communicate complex technical and architectural concepts to business and senior stakeholders in a clear, simplified manner.

•Review and approve test strategies, functional test cases, and data validation approaches.

•Manage risks and issues related to scope, data quality, regulatory commitments, and delivery timelines.

•Participate in product and platform evaluations (RFPs, PoCs) for data, analytics, and AI tooling.

•Partner with production support team to conduct root cause analysis, resolution, and preventive controls.

•Lead innovation and modernization initiatives, including data discovery, cataloguing, governance, and AI enablement.

•Drive productivity, efficiency & quality improvements across delivery and operational processes.

•Ability to design data architectures supporting NLP and AI-driven analytics.

FUNCTIONAL SKILLSETS

Analytics Domains

•Financial Crime Analytics Transaction Monitoring, Customer Due Diligence, Sanctions & Payments Screening

•Finance & Credit Risk Analytics Financial reconciliation, Allocation, Performance management, Regulatory and Management reporting, Credit risk exposure, NPL, Counterparty risk, Basel & IFRS9 input variables

Enterprise Data, Analytics & Unstructured Data Enablement

•Proven experience delivering large-scale analytics platforms within financial services spanning structured, semi-structured, and unstructured data

•Strong capability in requirements analysis and functional design for analytics use cases involving Transactional data, Investigator narratives, Case notes and alerts, Policy & Customer communications documents

•Experience defining data quality, governance, lineage, and reconciliation controls for both structured and NLP-derived datasets.

Unstructured Data & NLP-Enabled Analytics

•Ability to define data architectures and data flows that ingest, curate, and govern unstructured and semi-structured data within enterprise data platforms.

•Experience translating business requirements into NLP-enabled analytical use cases, such as Text classification and categorization, Entity & relationship extraction, Risk indicator identification, Summarization of alerts, cases, or documents

Knowledge Graph & Relationship Based Analytics

•Ability to design and govern an enterprise knowledge layer defining relationship taxonomies, entity resolution rules, and linkage logic

•Ability to translate use cases into relationship driven analytical designs, such as Network based risk identification, Hidden association and indirect exposure analysis, Related party and concentric risk detection

Requirements

Data Platforms & Architecture

•Open table formats: Apache Iceberg, Delta Lake, Apache Hudi

•Distributed processing & query engines: Spark, Trino/Presto, Hive

•Cost optimization strategies: tiered storage, lifecycle management, workload governance

Programming & Analytics

•SQL, BTEQ, GCFR

•Python (Pandas, NumPy)

•BI & visualization tools: Power BI, QlikSense

Data Integration & Quality

•Informatica suite: PowerCenter, BDM, IDQ, Enterprise Data Catalogue

•Data ingestion patterns: batch, CDC, streaming

•Data validation, quality controls, and reconciliation frameworks within environments

Governance, Risk & Compliance

•Data modelling, critical data elements, regulatory reporting

•Fine-grained data access controls (row-level, column-level, masking)

•Metadata management, lineage, and impact analysis

•Compliance with BCBS 239, MAS, AML/CFT, and internal data standards

Big Data Platforms

•Cloudera Hadoop distribution: Hive, Impala, Spark, Iceberg, Trino

Data engineer (Azure) – Synapse and Pyspark, Python, Datawarehouse and Azure Data Explorer, Azure Devops

Job Scope

• Design, review and development of Pyspark scripts. Testing, troubleshooting of data pipelines, orchestration.

• Designing and developing reports and dashboards in PowerBI,setting up access control with row level security DAX query experience.

• Establishing connections to source data systems such as on-prem databases, IOT devices, APIs.

• Managing the collected data in appropriate storage/data-base solutions e.g. file systems, SQL servers, Big Data platforms such as Hadoop, HANA, etc. as required by the specific project requirements.

• Design, development of relevant data pipelines using pyspark, copy data activities for batch ingestion.

• Performing data integration e.g. using database table joins, or other mechanisms at an appropriate level as required by the analysis requirements of the project.

• Deployment of pipeline artifacts from one environment to the other using Azure Devops.

Skills & Experience

• Bachelor’s Degree in Computer Science or Engineering with 2 years of experience in Azure Data engineering, Python, Pyspark or Big Data development.

• Sound Knowledge of Azure Synapse analytics for pipelines, orchestration, set up.

• 1-2 experience in Visualization design and development with Power BI. Knowledge on row-level security, access control.

• Sound experience in SQL, Datawarehouse, data marts, data ingestion with Pyspark and Python.

• Expertise in developing and maintaining ETL processing pipelines in cloud-based platforms such as AWS, Azure, etc. (Azure Synapse or data factory preferred)

• Team player with good interpersonal, communication, and problem-solving skills.

• Preferred to have Devops expertise.

Candidates without prior experience are welcome to apply as on the job training would be provided.

Working hours :

8:30am to 6pm (Monday to Friday) onsite, no hybrid option.

Key Responsibilities

·      Lead the design and implementation of ESG data, reporting, and analytics solutions across the organization.

·      Define technical architecture, data models, and integration frameworks for ESG data platforms.

·      Collaborate with business stakeholders, sustainability teams, and technology teams to translate ESG reporting requirements into scalable technical solutions.

·      Provide technical leadership to data engineering, analytics, and data science teams throughout the solution lifecycle.

·      Drive the development of ESG dashboards, KPIs, scorecards, and performance monitoring frameworks.

·      Oversee the ingestion, transformation, validation, and management of ESG datasets, including carbon emissions, sustainability metrics, and regulatory reporting data.

·      Establish and enforce ESG data governance, data quality, metadata management, and reporting standards.

·      Ensure ESG solutions align with global reporting frameworks and regulatory requirements, including GRI, SASB, TCFD, ISSB, and other emerging standards.

·      Review technical designs, code, and implementation approaches to ensure scalability, reliability, and maintainability.

·      Lead system integration efforts across internal and external ESG data sources and reporting platforms.

·      Support advanced analytics initiatives, including predictive modeling, sustainability insights, and ESG risk analysis.

·      Manage technical delivery, project planning, risk management, testing, and production deployments.

·      Mentor technical team members and promote best practices in data engineering, analytics, and ESG technology solutions.

·      Act as the primary technical advisor for ESG-related initiatives and stakeholder engagements.

Required Skills and Experience

·      Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Sustainability, Finance, or a related discipline.

·      5+ years of experience in data, analytics, or technology delivery roles, with at least 2 years in a technical leadership capacity.

·      Strong understanding of ESG concepts, sustainability reporting frameworks, and regulatory requirements.

·      Proven experience designing and delivering enterprise-scale data and analytics solutions.

·      Strong expertise in data architecture, data modeling, ETL/ELT processes, and data governance frameworks.

·      Hands-on experience with data visualization and analytics platforms such as Power BI or Tableau.

·      Proficiency in SQL and working knowledge of Python or R for data analysis and automation.

·      Familiarity with cloud-based data platforms and modern data engineering technologies.

·      Experience leading cross-functional teams within Agile delivery environments.

·      Strong knowledge of data quality, master data management, and reporting controls.

·      Excellent stakeholder management, communication, and leadership skills.

·      Ability to bridge business objectives with technical implementation and drive successful project outcomes.

Preferred Qualifications

·      Experience within financial services, banking, consulting, or sustainability-focused organizations.

·      Exposure to ESG data providers, sustainability reporting platforms, or regulatory reporting systems.

·      Knowledge of machine learning, predictive analytics, or advanced statistical modeling techniques.

·      Relevant certifications in ESG, sustainability reporting, cloud technologies, or data management are advantageous.

Job Duties & Responsibilities:

1. Work with large and complex financial datasets to develop end-to-end data science solutions for pricing various financial products, dynamic campaign optimization, and customer personalization.
2. Conduct research and literature review to assess and evaluate trade-offs between different quantitative algorithms and models.
3. Implement and train AI/ML models and optimize algorithm efficiency (GPU distributed computing, concurrent programming)
4. Refactor and document code into reusable libraries/ APIs/ tools, deploy machine learning ecosystems, and perform sub-system integration as required.
5. Integrate solutions into enterprise MLOps ecosystem and automate CI/CD pipelines for model lifecycle maintenance and monitoring.

Requirements:

1. Good understanding of the data science production life cycle with demonstrable experience working with structured, semi-structured and unstructured data.
2. Excellent software skills (Python, SQL variants) and knowledge in design patterns, code optimization, object-oriented programming.
3. Experience applying quantitative and machine learning algorithms for pricing and marketing.
4. Demonstrable expertise in some of the following domains - econometrics, statistical modelling, time-series analysis, signal processing, reinforcement learning, estimating causal relationships/ counterfactual effects, dynamic pricing.
5. Solid understanding of foreign exchange markets, including knowledge of currency pairs, market dynamics, and key drivers.
6. Hands-on experience in designing and executing digital campaigns and experimentation (A/B, Multivariate, Bandits, Sequential, quasi-experiments), evaluation methodologies (DiD variants), and conducting experiments to optimize campaign performance.

Act as the key local expert and lead user for planning-relevant master data, ensuring high consistency, quality and reliability of data used in Blue Yonder to support holistic planning of the semiconductor production network.

Serve as a critical interface between business, specialty departments, IT, and external IT service providers, driving further development, stabilization and continuous improvement of master data and planning IT solutions.

Utilize cost advantages of LCL.

• Drive end‑to‑end planning master-data quality and governance, ensuring reliable, usable data across business functions
• Act as technical lead for analytics and data solutions (Power BI, SQL Oracle/MSSQL, KNIME, databases, web/UI tools)
• Responsible for maintenance and enhancement of master data applications and UIs (e.g. Tivity) aligned with business needs
• Drive dashboards and advanced analytics to enable planning excellence, transparency, and self‑service insights
• Lead root cause analysis of complex data issues, implementing sustainable, preventive solutions
• Define, monitor and improve data governance frameworks, validation rules, workflows, KPIs and operating models
• Identify data quality trends and drive automation, structural improvements and scalability
• Ensure data solutions effectively support planning processes (demand planning, logistics interfaces, master data flows)
• Manage global and local stakeholder expectations, priorities and dependencies with clear, business‑oriented communication
• Motivate and develop the team by setting technical standards, best practices and ways of working
• Foster a culture of ownership, continuous improvement, analytical thinking and knowledge sharing
• Lead and contribute to PJM1‑SO projects, driving innovation, efficiency and advanced analytics adoption

Responsibilities:

• Data Infrastructure: Build, implement, and maintain scalable data pipelines and storage architectures.
• Data Quality: Maintain strong data integrity by ensuring reliability, consistency, and accuracy across all data processes.
• Performance Optimization: Enhance system efficiency by tuning data processing workflows and improving large-scale performance.
• Process Improvement & Mentoring: Strengthen data engineering practices while providing guidance and support to junior engineers.
• Cross-Functional Collaboration: Partner with business analysts, data scientists, and backend teams to deliver timely and actionable data solutions.

Qualifications:

• 5+ years’ experience in data engineering or backend roles, with strong programming skills in Python or Java/Scala.
• Hands-on experience with big data tools: Spark, Flink, Kafka, Airflow, and cloud platforms (AWS, GCP, Azure); proficient in SQL.
• Skilled in designing and maintaining data models, warehouses, and ETL/ELT pipelines for scalable, reliable systems.
• Knowledgeable in data quality, governance, security, and compliance best practices.

We regret only shortlisted candidates will be contacted. All applications will be handled confidentially. By submitting your application, you agree to the collection, use, retention, and disclosure of your personal information to prospective employers.

Wecruit Pte Ltd
EA License No: 20C0270
EA Personnel: Nyon Hock Sen (Aden)
EA Personnel Reg No: R24124070

·      Administer and manage Redis clusters for low-latency caching and real-time transaction processing.

·      Manage MongoDB clusters (replication, sharding) for scalable transaction and semi-structured data storage.

·      Working knowledge of Hadoop ecosystem (Hadoop, Hive, Pig, Oozie, Hbase, Flume, sqoop) using both automated tool sets as well as manual processes.

·      Support and maintain Hadoop (HDP)clusters for batch processing, analytics, and regulatory reporting.

·      Perform cluster lifecycle management provisioning, scaling, patching, and decommissioning nodes.

·      Ensure 24x7 availability and resilience of production systems supporting payment flows.

·      Manage and optimize Apache Kafka for high-throughput, real-time payment event streaming.

·      Support Apache NiFi for ingestionpipelines from upstream payment systems and external partners.

·      Work with AWS EMR for scalableprocessing of transaction data and reconciliation workloads.

·      Administer HDFS, ensuring optimal replication,storage utilization, and fault tolerance.

·      Manage OpenSearch / Elasticsearchclusters for transaction search, audit trails, and operational dashboards.

Required Skills & Experience

·      6–8 years of experience in Big Data / DataPlatform Engineering in enterprise environments.

·      Strong hands-on experience with:

·      Hadoop ecosystem (HDFS, YARN, MapReduce, HDP)

·      Apache Kafka (high-throughput environments)

·      Redis and MongoDB clusters

·      OpenSearch / Elasticsearch

·      Apache NiFi

·      AWS EMR + good knowledge of AWS Cloud.

·      Strong expertise in Linux system administration and scripting (Shell/Python)

·      Experience with Kerberos, data security, and access governance

·      Proven experience in handling high-volume, low-latency systems (preferably payments/trading) 

EA License No: 11C4879 / Registration ID : R1218583

Apar Technologies Pte Ltd, Singapore

Responsibilities

• Develop and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
• Build and support batch and real-time data processing solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Monitor, troubleshoot, and optimize data pipelines and workflows.
• Collaborate with business, analytics, and technology teams to deliver data-driven solutions.
• Support cloud-based data platforms and data warehousing initiatives.
• Develop automation and monitoring solutions to improve operational efficiency.
• Any other ad-hoc duties as assigned by supervisor.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 3+ years of Data Engineering or ETL development experience.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Hadoop, Spark, Kafka, or similar big data technologies.
• Knowledge of data warehousing and data modeling concepts.
• Experience with cloud platforms such as Azure Data Factory, Azure Synapse, Databricks, or Azure Data Lake.
• Strong problem-solving and stakeholder management skills.

Technical Skills

• Experience with Apache Flink or real-time streaming technologies.
• Exposure to Power BI, Tableau, Qlik, or other BI tools.
• Familiarity with AI/LLM frameworks such as LangChain, LangGraph, or RAG solutions.
• Experience within banking, financial services, or large enterprise environments.
• Tech Stack: Python, PySpark, SQL, Hadoop, Spark, Kafka, Azure, Databricks, Power BI/Tableau.

Interested applicants, please email your resume to Andre Chua Jing Ming

Email: andrechua@recruitexpress.com.sg

CEI Reg No: R1989053

EA Licence No: 99C4599

Recruit Express Pte Ltd

Responsibilities

• Develop and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
• Build and support batch and real-time data processing solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Monitor, troubleshoot, and optimize data pipelines and workflows.
• Collaborate with business, analytics, and technology teams to deliver data-driven solutions.
• Support cloud-based data platforms and data warehousing initiatives.
• Develop automation and monitoring solutions to improve operational efficiency.
• Any other ad-hoc duties as assigned by supervisor.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 3+ years of Data Engineering or ETL development experience.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Hadoop, Spark, Kafka, or similar big data technologies.
• Knowledge of data warehousing and data modeling concepts.
• Experience with cloud platforms such as Azure Data Factory, Azure Synapse, Databricks, or Azure Data Lake.
• Strong problem-solving and stakeholder management skills.

Technical Skills

• Experience with Apache Flink or real-time streaming technologies.
• Exposure to Power BI, Tableau, Qlik, or other BI tools.
• Familiarity with AI/LLM frameworks such as LangChain, LangGraph, or RAG solutions.
• Experience within banking, financial services, or large enterprise environments.
• Tech Stack: Python, PySpark, SQL, Hadoop, Spark, Kafka, Azure, Databricks, Power BI/Tableau.

Interested applicants, please email your resume to Andre Chua Jing Ming

Email: andrechua@recruitexpress.com.sg

CEI Reg No: R1989053

EA Licence No: 99C4599

Recruit Express Pte Ltd

About the Company

We are a premier global enterprise solutions and HR technology transformation partner working alongside hyper-scale multinational corporations, complex joint ventures, and global enterprise clusters. By combining advanced master data governance, predictive data modeling, and cutting-edge cloud architecture, we build secure and cohesive digital ecosystems that power intelligent HR operations. We focus on structural data integrity, cross-border data privacy engineering, and cultivating top-tier senior technical advisory talent in Singapore.

About the Job

We are seeking an experienced, elite-level SuccessFactors Data Lead to take complete strategic ownership of the end-to-end data strategy, architecture, and migration delivery across a complex multi-instance global enterprise. In this high-impact position, you will design the data models, master data governance rules, and quality thresholds across multiple SAP SuccessFactors instances, legacy platforms, and fragmented HR tools.

Your mission goes far beyond basic technical ETL (Extract, Transform, Load) execution. You must possess a deep functional command of how HR data behaves inside the SuccessFactors Employee Central engine. You will orchestrate complex multi-cycle data migrations, enforce regional data privacy standards (including GDPR and PDPA), and meticulously build a clean, trusted, and harmonized master data foundation engineered to support advanced human capital analytics and emerging GenAI/agentic frameworks.

Key Responsibilities

HR Data Strategy, Target-State Architecture & Harmonization

• Target-State Modeling: Define and own the enterprise HR data strategy across multiple SuccessFactors instances and legacy platforms, crafting target-state data schemas, position hierarchies, corporate taxonomies, and strict master data standards.

• Global Harmonization: Drive multi-instance data harmonization across global clusters, ensuring complete data consistency across Foundation Objects (business units, cost centers, job frameworks, pay structures), custom fields, and picklist values.

• Multi-Entity Calibration: Design a unified global master data framework that seamlessly accommodates regional legal entity nuances, joint venture structures, and cross-cluster reporting analytics frameworks.

End-to-End Data Migration & Cleansing

• Migration Orchestration: Blueprint, sequence, and execute large-scale data migration cycles (including initial profiling, mock data runs, differential delta mapping, reconciliation, and production cutovers) using specialized tools such as Spinifex, OpenText, or cloud ETL platforms.

• Data Integrity Gates: Formulate comprehensive mapping rules, structural transformation logic, automated validation scripts, and custom Excel data manipulation routines to bridge legacy data stores with SuccessFactors.

• Source-Level Remediation: Design and deploy automated data quality profiling and deduplication frameworks, establishing clear data health KPIs and collaborating with regional business teams to correct anomalies at the source.

• AI Readiness Hardening: Enforce strict data profiling thresholds to guarantee that processed employee records achieve the structural data maturity required for secure GenAI and agentic workflows.

Data Governance, Compliance & Integration Design

• Policy Enforcement: Establish and lead global data governance forums, defining clear data ownership boundaries, data steward accountabilities, centralized data dictionaries, and corporate business glossaries.

• Data Privacy Engineering: Define, configure, and audit strict localized data privacy controls, cross-border data transfer rules, and data masking protocols within SuccessFactors data models to ensure absolute compliance with GDPR and PDPA laws during testing and migration.

• Integration Data Contracts: Partner tightly with the Integration & Reporting Lead to define structural data contracts, API payload definitions, and master data synchronization paths between Employee Central and downstream systems (such as non-SAP HR payroll engines, corporate finance ledgers, and ITSM/ServiceNow configuration databases).

Team Leadership & Value Realization

• Workstream Leadership: Lead, mentor, and coordinate a cross-functional team of data analysts, migration developers, and data stewards across onshore (Singapore) and offshore execution hubs.

• Value Tracking: Establish value tracking metrics to measure data normalization successes, focusing on the reduction of manual reconciliation hours and the optimization of manager self-service data experiences.

Skills & Experience Required

Must-Have Skills (Mandatory for Skills Matching)

• SuccessFactors Data Mastery: Minimum 8+ years of hands-on HR technology experience with expert-level proficiency in HR data architecture, schema mapping, and master data management within the SAP SuccessFactors ecosystem.

• Employee Central Data Model Expertise: Deep functional, hands-on command of the SuccessFactors Employee Central (EC) data structure, including Foundation Objects, Position Management mechanics, and complex employment record schemas.

• Global Migration Lifecycle Execution: Proven experience leading the data track for massive, multi-instance global HR transformation programs, managing automated ETL cycles from legacy landscapes into SuccessFactors.

• Specialized HR Tooling: Practical experience with dedicated HR migration and profiling utilities (e.g., Spinifex, OpenText) or expert-level data manipulation scripting capabilities.

• Data Privacy & Compliance Governance: Demonstrable knowledge of implementing data privacy controls, access masking, and cross-border routing rules in compliance with GDPR and PDPA mandates.

Good-to-Have Skills (Preferred Differentiators)

• Official SAP SuccessFactors certification (Employee Central module preferred).

• Prior experience utilizing enterprise-grade master data management (MDM) platforms or automated data governance consoles.

• Familiarity with integration interface platforms (such as SAP Cloud Integration / CPI middleware) or downstream People Analytics reporting suites (Story Reports).

• Deep comprehension of data maturity indexing required to feed downstream Large Language Models (LLMs) or autonomous agent networks.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Architecture, Information Systems Management, Business Analytics, or a related technical/quantitative discipline.

• Outstanding consultative, workshop facilitation, and stakeholder management skills, with a proven ability to align competing priorities across global corporate divisions.

How to Apply

Please click on the 'apply' button to apply online. For more information, please reach out to Vievien Nathan.

EA License Number: 94C3609

Key Responsibilities

• Build and maintain scalable data pipelines using Python
• Write production-grade Python code specifically for data processing, transformation, and ETL workflows
• Perform data cleaning, preprocessing, and feature preparation for analytics and AI use cases
• Use data analysis and manipulation tools to handle large datasets efficiently
• Develop reusable Python modules for data ingestion and pipeline automation
• Perform exploratory data analysis (EDA) to understand data patterns and quality issues
• Optimize data workflows for performance, scalability, and reliability
• Support data requirements for AI/ML and Generative AI systems
• Build data services and APIs to support downstream AI applications
• Ensure data quality, consistency, and observability across pipelines

Must have skills:

Python & Data Libraries (Hands-on Experience Mandatory)

Candidates must have solid practical experience with:

• Pandas — data manipulation, transformation, and analysis
• NumPy — numerical operations and array-based processing
• Matplotlib — data visualization and reporting
• scikit-learn — basic ML workflows and model evaluation
• PyTorch — deep learning and AI model experimentation

AI / Generative AI Enablement

• Prepare and structure datasets for ML and LLM-based systems
• Support integration of AI models into data pipelines and applications
• Enable workflows for Generative AI use cases (RAG systems, agent workflows)
• Work with multiple AI model providers:
• OpenAI
• Anthropic
• LLaMA
• Mistral
• Exposure to AI orchestration frameworks such as LangChain, AutoGen, and CrewAI

Core Requirements

• Solid hands-on Python coding expertise focused on data systems (critical requirement)
• Ability to write clean, efficient, production-grade Python code
• Thorough understanding of data structures, ETL pipelines, and data workflows
• Experience working with large-scale structured and unstructured data
• Solid SQL skills for data extraction and manipulation
• Understanding of data modeling and analytics workflows
• Ability to support end-to-end data-to-AI pipelines

Good to Have

• Experience with big data or distributed processing systems
• Understanding of vector databases and embedding-based retrieval systems
• Experience building APIs or services for data/AI systems
• Familiarity with cloud platforms (AWS, Azure, GCP)
• Exposure to production monitoring and data observability tools