Total 806 data entry job vacancies

Purpose of Role:

Assist in the development of ILS deliverables for ILS programmes that meets customer requirements efficiently and on time.

Responsibilities:

• Responsible for data entry and document assembly or extraction.
• Creating and populating data into templates for codification
• Processing and doing MMD data entry into software system • May be required occasionally to be at external site to assist for coordination of delivery of items to store
• May be required occasionally to be at external site for coordination of training
• Occasional lifting and carrying of documents or parts is required.

Requirements:

• Good proficiency in MS Office Suite
• Experience in Document Control for engineering projects will be advantageous
• Good written, verbal communication skills in English is required
• Able to interact effectively with all levels of staff both internally and externally
• Able to work independently
• Willing to commit to 2 years contract

Additional Information:

• Location: 16 Benoi Road (Island-wide transport provided)
• 5 days work week
• Working Hours: 7.25am to 5.00pm

About the job

Reporting to the Data and Analytics Lead, the successful candidate will be responsible for the following:

Data Management & Transformation

• Designing and developing new data pipelines and managing existing data pipelines that extract data from various business applications, databases, and external systems.
• Implementing data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data.
• Transforming data into the desired format by applying data cleansing, aggregation, filtering, and enrichment techniques.
• Establishing the governance of data and algorithms used for analysis, analytical applications, and automated decision-making.
• Manage the logical and physical data models to capture the structure, relationships, and constraints of relevant datasets.
• Ensuring compliance with security and governance best practices.

Optimization & Automation

• Implementing and maintaining continuous integrations and continuous delivery pipelines for deployments and cloud resource provisioning.
• Optimising data pipelines and data processing workflows for performance, scalability, and efficiency.
• Optimising models and algorithms for data quality, security, governance, performance and scalability needs.
• Routinely assessing processor and storage capacity across the data warehouse and extract, transform & load platforms, including capacity planning and forecasting.
• Monitoring and tuning the data system, identifying and resolving performance bottlenecks and issues, and implementing caching and indexing strategies to enhance query performance.
• Monitoring the platform for credit consumption and housekeeping.
• Supporting the deployment and maintenance of AI solutions in the data platform.

Collaboration

• Working with the data lead and business users to manage data as a business asset.
• Guiding the business users to create and maintain reports and dashboards.

Requirements:

• Bachelor’s degree in computer science, data science, software engineering, information systems, or related quantitative field; Master’s degree preferred.
• At least ten years of work experience in data management disciplines, including data integration, modelling, optimisation, and data quality, or other areas directly relevant to data engineering responsibilities and tasks.
• At least four years of work experience in designing and implementing data architectures in Azure cloud services and Databricks.
• Strong proficiency in Python (PySpark) and SQL programming; experience with Java or Scala is an advantage.
• Experience with relational and non-relational databases, including SQL and NoSQL, is a must, while familiarity with legacy databases such as Oracle is preferred.
• Experience using Azure DevOps, Databricks LakeFlow Jobs for DevOps practices, including version control, CI/CD and pipeline deployment.
• Experience with data catalogue tools, including Unity Catalogue and Microsoft Purview.
• Familiarity with BI & visualisation tools such as Power BI and Python visual packages for data analytics is preferred.
• Experience supporting AI/ML model deployment, feature engineering and production inference is preferred.
• Align with and demonstrate the company's Core Values, which are Integrity, Teamwork, Accountability, Agility, and Ambition.

The Reference Data Specialist is responsible for instructions of client deliverables, coding and data input validation focusing on specific industry/category or set of categories (e.g. Beverages, Food, Health and Beauty Products). As a Reference Data Specialist  at NielsenIQ, you create and deliver client outputs in a correct and timely manner.

• Responsible for instructions of client deliverables, coding and data input validation focusing on specific industry/category or set of categories.
• Does cross coding and item-related quality checks on a daily basis
• May act as a back-up to other coders when needed; Keep track/document special instructions related to category assignment
• Plan and execute a complex daily personal workload and support to meet departmental and company schedules
• Ensure accuracy throughout the process, from validation before database setup to managing samples, and identify areas for improvement
• Answer external questions and act as a liaison with everyone from clients to stakeholders; To be able to identify, investigate and coordinate the resolution of data, process, or product related queries
• Willing to be trained to be a category expert

Job Description: Data Engineer

As a Data Engineer at D-SIMLAB Technologies, you will play a key role in deriving insights from large datasets to optimize semiconductor manufacturing processes. This role focuses on data extraction, analysis, and actionable insights, while collaborating with cross-functional teams to develop and deploy D-SIMLAB’s software products as well as provide professional consultation services to our clients. The ideal candidate is someone who excels in data-driven analysis and is eager to work on high-impact projects in the semiconductor industry.

Key Responsibilities:

• Data Extraction and Analysis:
  Collect, clean, and analyze large datasets from semiconductor manufacturing environments to ensure data consistency, completeness, and correctness.
  Use statistical methods and data mining techniques to identify patterns, trends, and anomalies that impact production performance.
• Performance Evaluation:
  Analyze the gap between planned and actual production outcomes. Identify root causes for performance deviations and recommend data-driven strategies for process improvement. Develop reports and visualizations to present findings to clients and internal stakeholders.
• Data-Driven Insights:
  Provide actionable insights based on data analysis to optimize semiconductor manufacturing processes. Work closely with clients to understand their specific needs and translate data findings into operational and strategic improvements.
• ETL and Data Modeling:
  Build and maintain ETL processes to integrate data from various sources into D-SIMLAB’s data models. Collaborate with simulation modeling teams to ensure accurate and reliable data is fed into models.
• Consultation Services:
  Act as a trusted advisor to clients by offering data-driven recommendations for enhancing their production efficiency. Present analyses and proposals to both technical and non-technical stakeholders, ensuring clarity and actionable takeaways.
• Data Visualization and Reporting:
  Create dashboards and reports using advanced data visualization tools to communicate key insights and trends effectively. Continuously improve reporting mechanisms to provide real-time insights and support ongoing optimization efforts.
• Collaboration with Teams:
  Work with Software Engineers, Data Scientists, and other cross-functional teams to ensure that data insights align with product goals and customer needs. Contribute to product enhancement initiatives, focusing on the improvement of data-related components and analytics.

Candidate Requirements:

• Education:
  Bachelor’s/Master’s degree in Data Science, Industrial Engineering, or a related field.
• Experience:
  Strong analytical skills with a minimum of 3 years of experience in data analysis, preferably in an industrial environment. Semiconductor experience is a plus.
• Technical Skills:
  Proficiency in SQL and Microsoft Excel.
  Experience with data visualization tools (e.g., Power BI, Tableau) is highly desirable.
  Experience with ETL processes and database management.
• Analytical Abilities:
  Strong problem-solving skills and the ability to handle large datasets and deliver actionable insights to improve operational performance.
• Soft Skills:
  Ability to work collaboratively in a multicultural, multidisciplinary team.
  Excellent communication and presentation skills, with the ability to convey complex data insights to non-technical stakeholders.
• Willingness to Travel:
  Willing to travel for international projects and collaborate closely with clients and internal teams.

Please ensure your profile on the portal is updated with your job experience and education as these fields are being used for the purpose of filtering and shortlisting.

Key Responsibilities

Integration & Workflow Engineering

• Analyse and re-platform existing workflow-based integrations from one platform to another while maintaining functional parity.
• Implement orchestration logic including triggers, conditional routing, retries, exception handling, and state management.
• Design and implement automation patterns to handle platform limitations (e.g., looping, batching, pagination, throttling).
• Ensure workflows are idempotent, fault tolerant.
• Support environment-based deployments (DEV / UAT / PROD) with configuration-driven designs.

Data Engineering & ETL

• Design and build ETL pipelines for ingesting flat files (e.g., CSV) into relational databases.
• Handle schema validation, basic schema evolution, data quality checks, and error reconciliation.
• Optimize data ingestion for performance, scalability, and reliability.
• Collaborate with application teams to understand upstream and downstream data dependencies.

Cloud & Big Data Platform Contributions

• Build and maintain data pipelines on cloud-based big data platforms using distributed processing frameworks.
• Contribute to Lakehouse-style data storage that supports both batch and streaming data.
• Work with modern table formats that support incremental processing, versioning, and historical queries.
• Support use cases such as append-heavy datasets, high-write event data, and analytical queries.

Operations, Quality & Observability

• Implement logging, monitoring, and alerting for workflows and data pipelines.
• Support operational readiness including runbooks, deployment procedures, and rollback strategies.
• Participate in root-cause analysis and continuous improvement of data pipelines.
• Ensure adherence to data governance, security, and compliance standards.

Required Skills & Experience

Technical Skills

• Strong experience with workflow orchestration / integration platforms.
• Solid understanding of ETL concepts and hands-on experience with file-based ingestion.
• Proficient in SQL and working knowledge of relational databases (e.g., SQL Server or equivalent).
• Experience with distributed data processing frameworks (e.g., Spark).
• Familiarity with streaming and batch data processing concepts.
• Practical experience with cloud platforms and managed data services.
• Understanding of CI/CD principles for data and integration workflows.

Programming & Automation

• Experience with scripting or programming languages commonly used in data engineering (e.g., Python).
• Ability to build reusable utilities for batching, retries, pagination, and error handling.
• Experience with REST APIs and structured data formats (JSON, CSV).

Desired (Good to Have)

• Exposure to modern Lakehouse table formats supporting incremental processing and time travel.
• Experience with stream processing engines (e.g., Flink or Spark Streaming).
• Familiarity with query engines used for analytical access.
• Experience working in regulated environments (banking, financial services, or manufacturing).
• Knowledge of data observability and quality frameworks.

Experience Level

• Typically, 3–5 years of relevant experience in data engineering, integration engineering, or similar roles.
• Proven ability to work independently on moderately complex problems while collaborating within a larger team.

Soft Skills & Competencies

• Strong analytical and problem-solving skills.
• Ability to understand existing systems and translate requirements into working solutions.
• Clear communication with technical and non-technical stakeholders.
• Detail-oriented with a focus on reliability and maintainability.
• Comfortable working across multiple teams and applications.

Here’s a shorter, more attention-grabbing version:

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This is a full-time role for a Data Engineer. The Data Engineer will be responsible for designing and implementing scalable data pipelines, developing and maintaining data models, performing Extract, Transform, Load (ETL/ELT) processes, and managing modern data warehousing and analytics solutions—primarily utilizing Microsoft Fabric and Databricks. The role also includes building and operationalizing lakehouse architectures, ensuring data governance and security best practices, and analyzing complex datasets to support business decision-making. Collaboration with cross-functional teams to deliver high-quality data solutions is essential. 

Qualifications 

• Strong proficiency in Data Engineering, Data Modelling, and building Lakehouse/Data Warehouse solutions 

• Demonstrates strong proficiency in Medallion architecture and metadata-driven concepts. 

• Hands-on experience with Microsoft Fabric (Data Factory, Lakehouse, Warehouse, OneLake, Data Activator, Power BI integration) 

• Hands-on with Databricks (Spark, Delta Lake, DLT pipelines, Unity Catalog) 

• Proficiency in ETL/ELT development and orchestration within Azure ecosystem 

• Strong skills in Data Analytics using SQL, Python or PySpark 

• Knowledge of Cloud-native data platforms including Azure Data Lake, Azure Synapse, Azure Data Factory, Azure Storage, etc. 

• Understanding of data governance, security, and CI/CD best practices for cloud data platforms 

• Strong problem-solving and analytical skills 

• Ability to work independently and remotely in a fast-paced environment 

• Bachelor’s degree in computer science, Information Technology, or related field 

Company Overview

TECHCOM SOLUTIONS, founded in 2003, is a Singapore-based IT total solutions provider serving government agencies, MNCs, system integrators, educational institutions, and BFSI across Singapore and APAC. We specialize in outsourcing, managed services, and staffing solutions.

Job Summary

You will develop and optimize data integration pipelines and ETL/ELT codes using Teradata SQL, Informatica, and Apache Spark to support data warehousing and analytics. You will also automate workflows and enhance application performance to drive efficient data operations.

Responsibilities

• Develop ETL/ELT codes using Teradata SQL, Informatica PowerCenter, Apache Spark, QueryGrid, and Trino to transform and load data into Teradata data warehouses and datamarts
• Optimize and tune performance of complex data applications to reduce resource consumption and improve efficiency
• Design and implement data pipeline orchestration frameworks using GCFR, shell scripting, and BTEQ to automate end-to-end data workflows
• Integrate automation frameworks with control systems to ensure reliable and scalable data processing
• Apply CDC (Change Data Capture) concepts, preferably with Java-based implementations, to support real-time data updates
• Utilize Apache Spark and Hadoop ecosystem tools (Hive, Kafka, NiFi, Iceberg, Impala) to manage and process large-scale data sets
• Write and maintain shell scripts and BTEQ scripts to support data operations and automation tasks

Preferred competencies and qualifications

• 6+ years of relevant experience in data engineering and ETL development

Job Description:

Combine AI/ML and Technology knowledge to implement highly scalable solutions to solve real-world problems in areas such as Generative AI, Personalization, Image Recognition, Speech Recognition, Natural Language Processing, Best Next Action, and Time Series predictions

• Build solutions from identifying business problems, to collecting training dataset, to analyzing data, to modeling, to validating and to delivering production grade solutions at scale.
• Research latest related academic papers and test nascent ideas which can potentially solve new business problems and enhance existing solutions.
• Responsible for developing and defining deployment strategies for production quality AI solutions in Cloud infrastructure at scale
• Drive both high-level and detailed technical designs and conduct design reviews as needed
• Responsible for health and quality of the code across the portfolio, including leadership over innovation, functional testing, and CI/CD tool integration
• Provide technical mentorship to team members at junior levels
• Actively participate in business and technical discussions and forums

We're building an AI-powered research platform for institutional investors. Our platform turns vast amounts of market, alternative, and proprietary data into actionable intelligence — powered by AI agents that depend on clean, reliable, real-time data to do their job.

We need someone to own data. Not manage a team that does data. Own it — from finding the right sources, to getting them flowing, to making sure they stay healthy at scale.

Today we ingest from hundreds of sources. That number is growing fast. The sources are diverse: real-time market feeds, regulatory filings, news, social sentiment, alternative datasets, and proprietary client data. Some are free APIs. Some are $10K/month enterprise contracts. Some are clients pushing their own data into our platform. Every one of them is different, and most of them will break in ways you don't expect.

You'll evaluate vendors, negotiate deals, build integrations, monitor quality, track costs, and make the call on what's worth paying for. When something breaks at 2 AM, you'll know why before the alert finishes firing.

This is an end-to-end ownership role. No handoffs.

Responsibilities

• Build and maintain integrations with a large and growing number of external data sources — APIs, WebSockets, file drops, streams, scrapers, and formats you haven't seen yet
• Evaluate and compare data vendors across quality, reliability, coverage, cost, and terms of service
• Negotiate contracts and manage commercial relationships with data providers
• Design and operate high-throughput ingestion pipelines handling mixed workloads (real-time, near-real-time, batch, event-driven)
• Build monitoring that tells you — before anyone else — when data is late, wrong, incomplete, or drifting
• Manage data quality at scale: anomaly detection, cross-source validation, schema drift detection, gap filling
• Handle both structured data (time-series, tabular) and unstructured data (documents, text, images) with appropriate extraction and storage
• Track costs per source, usage per consumer, and ROI — recommend what to keep, upgrade, or cancel
• Build tooling that makes adding the next data source faster than the last one
• Use AI tools aggressively in your daily work — for code generation, testing, documentation, anomaly analysis, and anything else that makes you faster

Requirements

You've done this before:

• 5+ years building data pipelines that run in production, 24/7, with real SLAs
• Deep hands-on experience with SQL databases and time-series data
• Python as your primary language, comfortable with async programming
• You've integrated with dozens of external APIs and dealt with the reality of unreliable vendors, changing schemas, rate limits, and bad documentation
• You've built monitoring and alerting for data systems — not as an afterthought but as part of how you work

You think about the whole picture:

• You don't just connect to an API. You think about what happens when it goes down, when the schema changes, when the data is wrong, when the bill doubles
• You understand that data has a cost and a value, and not every source is worth keeping
• You've worked with data vendors commercially — contracts, pricing tiers, usage negotiations

You use AI daily:

• AI coding tools are part of your workflow today, not something you're curious about
• You can articulate specifically how AI makes you faster and where it doesn't help
• You'd be frustrated if you couldn't use AI in your work

Nice to have

• Experience with financial market data
• Experience with streaming systems (Kafka or similar) at scale
• Vector database or embedding pipeline experience
• Experience with unstructured data extraction (PDFs, documents, NLP)

Perks & Benefits

• Senior individual contributor role with full ownership of the data domain
• Direct access to leadership — no bureaucracy, fast decisions
• AI tools provided and encouraged across all work
• Remote-friendly, async-first
• Compensation commensurate with experience

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As a Data Scientist (Smart Manufacturing and AI) at Micron, you will leverage advanced techniques from mathematics, statistics, and information technology to improve semiconductor product quality, yield, and productivity. You will develop predictive models, actionable insights, and AI-enabled solutions that address large-scale manufacturing and quality challenges involving die-level, wafer-level, test, and quality/customer-related data.

Your work will directly help prevent customer-impacting quality events, reduce escapes, accelerate root-cause analysis and corrective actions, detect distribution shifts in manufacturing and field signals, and enable intelligent automation across Micron’s global fabs, bridging factory learning with customer-facing product quality outcomes.

Responsibilities and Tasks

Product Quality (PQ) Predictive Solutions:

• Build and productionize ML models (classification/regression) for product-quality prediction (e.g., escape prevention, failure-mode prediction, risk scoring) across manufacturing and post-manufacturing data.

• Develop anomaly and drift detection/monitoring (e.g., Isolation Forest, autoencoders/VAEs) to identify emerging quality signatures in fab, test, and field/customer signals.

• Apply text analytics (embeddings, clustering, classification, summarization) to quality narratives, customer feedback, FA notes, and engineering logs.

Agentic AI Development:

• Contribute to the design and implementation of multi-agent systems for PQ workflows, including automated root-cause analysis and process optimization.

• Optimize for performance, latency, and token efficiency.

• Assist in implementing tool-using capabilities, function calling, and agent memory systems.

• RAG-LLM Development: Develop advanced retrieval strategies including hybrid search, re-ranking, and contextual compression.

Software Engineering and Productionalization:

• Develop robust, maintainable code and analytical pipelines suitable for high-volume manufacturing environments.

• Optimize system performance, latency, and token efficiency.

• Collaborate with senior team members on testing, deployment, and monitoring.

• Clearly communicate analytical insights, model behavior, and trade-offs to both technical and non-technical customers to enable data-driven decisions.

• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.​

• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.​

Required Qualifications & Skills

Education/Experience:

• Bachelor’s in Computer Science, Data Science, Operations Research, Mathematics, or a related highly quantitative field.

• Strong desire to grow a career as a Data Scientist in advanced, highly automated industrial manufacturing.

Technical Skills:

• Proficiency in Python and experience extracting and manipulating data using SQL.

• Solid foundation in statistics and machine learning (supervised/unsupervised learning, model evaluation, feature engineering).

• Experience with SQL for data extraction and manipulation.

• Experience with version control (Git).

• Familiarity with building interactive data applications or dashboards (e.g., Streamlit, PowerBI, or similar).

• Strong verbal and written communication skills, with the ability to explain complex analytical results clearly.

• Ability to apply baseline digital fluency and role‑appropriate AI literacy to use AI‑enabled tools responsibly and effectively for research, analysis, content creation, problem‑solving, operational tasks, and achieving business outcomes

Preferred Qualifications

• Exposure to LLMs, agentic AI frameworks, or RAG pipelines (internship experience, academic, or personal projects welcome).

• Experience with time-series data, process/manufacturing data, or handling concept drift/imbalanced datasets.

• Familiarity with cloud platforms (Google Cloud Platform, AWS, or Azure) and data visualization tools (Tableau, Power BI).

• Exposure to ETL tools, containerization (Docker), or web frameworks (Angular, React, FastAPI).

As a Senior Data Engineer, you will be working on:

• Translate data requirements from business users into technical specifications.

• Collaborate with partner agency’s IT teams on technology stack, infrastructure and security alignment.

• Build out data product as part of a data team:

• Architect and build ingestion pipelines to collect, clean, merge, and harmonize data from different source systems.

• Day-to-day monitoring of databases and ETL systems, e.g., database capacity planning and maintenance, monitoring, and performance tuning; diagnose issues and deploy measures to prevent recurrence; ensure maximum database uptime;

• Construct, test, and update useful and reusable data models based on data needs of end users.

• Design and build secure mechanisms for end users and systems to access data in data warehouse.

• Research, propose and develop new technologies and processes to improve agency data infrastructure.

• Collaborate with data stewards to establish and enforce data governance policies, best practices and procedures. • Maintain data catalogue to document data assets, metadata and lineage.

• Implement data quality checks and validation processes to ensure data accuracy and consistency.

• Implement and enforce data security best practices, including access control, encryption, and data masking, to safeguard sensitive data.

What we are looking for:

• A Bachelor’s Degree, preferably in Computer Science, Software Engineering, Information Technology, or related disciplines.

• Deep understanding of system design, data structure and algorithms, data modelling, data access, and data storage.

• Demonstrated ability in using cloud technologies such as AWS, Azure, and Google Cloud.

• Experience with Databricks.

• Experience in designing, building, and maintaining batch and real-time data pipelines.

• Experience with orchestration frameworks such as Airflow, Azure Data Factory.

• Proficiency in working with Python, Shell Scripts, and SQL.

Preferred requirements:

• Familiarity with building and using CI/CD pipelines.

• Familiarity with DevOps tools such as Docker, Git, Terraform.

• Experience with implementing technical processes to enforce data security, data quality, and data governance.

• Familiarity with government systems and government's policies relating to data governance, data management, data infrastructure, and data security.

• Experience in Climate and Weather domains will be an advantage.

This will be a 2-years direct contract position.

Responsibilities

• Data science developments.
• Lead project scoping discussions.
• Translate complex business requirements into technical solutions.
• Design, implement, and optimise end-to-end ML/AI solutions with improved performance and scalability.
• Systematically benchmark and evaluate multiple approaches, applying advanced techniques to solve complex business problems.
• Architect scalable solutions using cloud-based services and best practices, and design and implement robust data pipelines and LLM/MLOps workflows that improve efficiency and reduce operational costs.
• Deploy models to production environments with comprehensive monitoring, alerting, and maintenance procedures.
• Champion responsible AI practices, proactively identifying and mitigating ethical and governance risks.
• Collaborate with peers and stakeholders to ensure tasks are executed effectively, addressing minor conflicts constructively and respectfully, and suggesting improvements that enhance team effectiveness.

Requirements

• Bachelor's degree or higher in Data Science, Computer Science, Statistics, Economics, Quantitative Social Science, or related disciplines. Minimum 2 years of relevant experience in data science and/or public sector.
• Able to business use cases into analytical problems and identifying appropriate data sources.

Proficient in

• Writing scripts for data preparation and analysis, using modern analysis tools and programming methodologies.
• cleaning, imputing, and correcting anomalies in the collected structured or unstructured data.
• Exploring and analysing datasets, applying probability and statistical methodologies and techniques to discover insights from the data.
• Building machine learning models to identify, recognise patterns, and make predictions.
• Design principles and use of visualisations to best convey the intended information.
• Developing data visualisation from standalone graphs and charts on to highly customised tools and apps while tightly integrated into the data systems for real time visualisation of information.
• Translating results from analysis of work into actionable recommendations for stakeholders.
• Communicating results and business impact from analysis work in a coherent data story for stakeholders.

Ethos Search Associates Pte. Ltd.

EA License No: 13C6655

EA Reg No: R1988580 Jacky Chong

Key Responsibilities

• Develop and implement data analytics solutions to convert raw structured and unstructured data into meaningful insights.
• Apply data mining, data modeling, machine learning, and natural language processing (NLP) techniques to solve business challenges.
• Design and maintain scalable data pipelines, ETL processes, and data platforms to ensure data quality, accessibility, and reliability.
• Build dynamic dashboards, reports, and visualizations to communicate key findings and support decision-making.
• Establish data governance frameworks, data standards, metadata management, and asset catalogs to improve data consistency and compliance.
• Collaborate with cross-functional stakeholders to deliver data-driven solutions and optimize business processes.
• Evaluate emerging AI technologies and implement innovative solutions that enhance operational efficiency and business performance.

Preferred Qualifications

• Master's degree or above in Computer Science, Data Science, Engineering, Statistics,  Business Analytics or a related field.
• Proven experience in Data Engineering, Data Analytics, Machine Learning, AI Applications, or Data Governance.
• Strong proficiency in SQL, Python, and data visualization tools such as Tableau or Power BI.
• Experience building ETL pipelines, data lakes, APIs, and large-scale data platforms.
• Solid understanding of machine learning, predictive analytics, data modeling, and NLP techniques.
• Excellent analytical, problem-solving, and stakeholder management skills.

Interested candidate please click "APPLY" to begin your job search journey.

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PERSOL Singapore Pte Ltd

•RCB No. 200007268E • EA License No. 01C4394

•Registration ID: Heah Sian Wei R23117518

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