Total 10 job vacancies by Base Camp Digital Pte. Ltd.

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.

1. ETL Development

• Design, develop, and maintain end-to-end ETL/ELT processes using Azure Data Factory (ADF) and Azure Databricks.
• Write complex, high-performance T-SQL queries, stored procedures, and functions to transform raw data into actionable insights. Optimize data loads for performance and scalability within Azure SQL Database and Synapse Analytics.

2. Quality Assurance & Testing

• Develop and execute comprehensive Data Validation scripts to check for data completeness, transformation logic, and referential integrity. Conduct Source-to-Target mapping validation and performance testing on large datasets. Identify, document, and track data anomalies and bugs, working closely with the engineering team to resolve bottlenecks.

3. Infrastructure & Collaboration

• Work within an Agile environment, collaborating with Data Architects and Business Analysts to understand requirements. Manage version control and CI/CD deployments using Azure DevOps. Monitor pipeline health and set up automated alerts for failure or data drift.

Technical Requirements

SQL Mastery: Deep expertise in SQL (T-SQL/PL-SQL) for data manipulation, optimization, and complex joins.

Azure Data Stack: Hands-on experience with Azure Data Factory, Azure Data Lake Storage (ADLS), and Azure SQL Database. (Knowledge of Databricks/PySpark is a major plus).

Testing Tooling: Experience with data testing tools (e.g., dbt, Great Expectations) or custom Python/SQL testing scripts.

Cloud Infrastructure: Understanding of Azure environment security, Managed Identities, and Key Vaults.

Education: Degree in Computer Science, Information Technology, or a related field, plus 3–5 years of relevant experience.

Engineering Manager

Job Type: Permanent
Location: Singapore, Hybrid Work Model
Salary: SGD 168,000 – 180,000 per annum + Insurance Benefits

We are seeking an experienced and hands-on Engineering Manager to lead a high-performing team delivering scalable, data-intensive applications. This role requires a strong balance of technical leadership, people management, and strategic thinking, with the ability to stay close to the code while driving engineering excellence.

You will play a key role in shaping architecture, improving engineering practices, and ensuring the successful delivery of high-quality software in a fast-paced, globally distributed environment. Collaboration across EU and US time zones will be required.

Key Responsibilities:

• Technical Leadership: Define architecture and platform strategy, drive best practices in scalability, performance, and reliability, and contribute hands-on to development and code reviews.
• Data & Platform Engineering: Design and optimize data models, pipelines, and analytical workloads across large-scale systems, ensuring high performance and maintainability.
• Stakeholder Management: Translate business needs into technical solutions, communicate trade-offs clearly, and coordinate delivery across global teams (EU & US time zones).
• People Leadership: Lead, mentor, and grow a high-performing engineering team, driving hiring, performance management, and a culture of ownership and continuous improvement.

Required Technical Skills & Experience:

Core Engineering:

• Strong experience with TypeScript across Back-End and Front-End development.
• Expertise in building and maintaining RESTful and/or GraphQL APIs.

Data Engineering & Databases:

• Hands-on experience with analytical/columnar databases (e.g., ClickHouse, BigQuery, Redshift, DuckDB).
• Strong proficiency in PostgreSQL and MS SQL Server, including performance tuning and query optimization.
• Experience designing and optimizing large-scale data processing systems.

DevOps & Infrastructure:

• Strong experience with Kubernetes (deployment, scaling, troubleshooting in production).
• Hands-on experience with Helm and containerized application deployment.
• Familiarity with cloud platforms (preferably Azure, but AWS/GCP also relevant).
• Experience building and maintaining CI/CD pipelines using GitHub Actions.

Preferred:

• Experience working with OMOP CDM (Observational Medical Outcomes Partnership Common Data Model), including data modelling, ETL pipelines, and schema design.
• Exposure to healthcare data standards (e.g., HL7 FHIR, ICD, SNOMED, LOINC) is a plus.
• Experience working with large-scale unstructured data or analytics platforms.
• Familiarity with modern Front-End frameworks and analytics-driven UI components.
• Background in data-intensive platforms, research, or real-world data applications.
• Contributions to open-source projects is a plus.

Role Summary:

We are seeking a hands-on Data Engineer to spearhead our Databricks Proof of Concept (POC) on AWS. You will be responsible for the initial environment setup, security configuration, and designing the framework for our future data platform. Working across diverse AWS and external data sources, you will build a unified, production-ready Databricks environment that validates the platform for broader adoption.

Key Responsibilities:

Platform & Infrastructure Setup

• Configure Databricks workspace integration with AWS (S3, IAM, VPC).
• Design and implement a scalable Medallion Architecture using Delta Lake (Bronze / Silver /Gold layers).
• Build automated ingestion pipelines leveraging Databricks Autoloader.

Data Engineering

• Cleanse and transform raw data from S3, RDS, and APIs into Delta tables.
• Optimize Spark jobs for performance, reliability, and cost-efficiency.
• Connect diverse AWS and external data sources into a unified Databricks environment.

Governance &Evaluation

• Establish data governance standards using Unity Catalog. (Good to have — not a hard requirement for the POC.)
• Configure Databricks Clusters with Autoscaling and Spot Instances to manage AWS spend.
• Define and evaluate POC success metrics covering performance, cost, and ease of use.
• Set up an end-to-end pipeline (Source → S3 → Databricks → BI Tool) within agreed timelines.

Qualifications and Experience:

Must-Have Skills

• Databricks Mastery — Expert knowledge of Delta Lake and Medallion Architecture (Bronze /Silver / Gold).
• PySpark / Spark SQL — Ability to write and optimize Spark code; Python strongly preferred over Scala or R.
• AWS Infrastructure — Deep understanding of S3 (bucket policies, storage), IAM(roles and policies) for secure Databricks access.
• Data Ingestion —Hands-on experience with Databricks Autoloader or Unity Catalog for managed data governance.
• 3–5+ years of professional experience in Data Engineering with PySpark / SQL.

Good to Have

• Networking — VPC configuration and AWS networking concepts.
• AWS Glue or Amazon EMR experience.
• Databricks Certified Data Engineer Professional certification.
• Experience connecting OOTB BI tools to Databricks environments.

Role Summary:

We are seeking for a strategic Data Engineer focused on building and scaling enterprise data platforms and pipelines for an upcoming project. The position combines strong data engineering fundamentals with data strategy, architecture, and governance, while also driving AI-enabled innovation to improve decision-making and user outcomes.

It requires technical expertise, analytical thinking, and stakeholder collaboration across product, engineering, and business teams. The role also emphasizes a mindset of ownership, informed decision-making, and continuous learning to support evolving technologies and product growth.

Key Responsibilities:

This role spans end-to-end data engineering, AI, and platform delivery, focused on building scalable data systems that support product, analytics, and policy decisions.

You will design and maintain data pipelines, platforms, and models, deliver analytics and data products, and work closely with stakeholders to turn business needs into actionable insights. The role also includes developing and operationalising AI/ML and LLM-based solutions, supported by strong MLOps practices.

On the platform side, you’ll implement modern data architectures and DataOps practices (e.g., automation, streaming, data observability) to ensure high data quality, reliability, and performance, while managing technical debt.

You’ll also ensure governance, security, and compliance, proactively managing risks in data systems.

Beyond technical execution, the role requires ownership and leadership—driving projects independently, mentoring others, collaborating cross-functionally, and contributing to broader strategy, technology decisions, and organisational impact.

Requirements:

Experience & Education

• Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
• At least 5 years of experience in data engineering.
• Proven experience delivering data platforms or products in large-scale, enterprise environments.

Technical Skills

• Strong expertise in data modelling (OLTP, OLAP, dimensional modelling).
• Experience with cloud data platforms and tools (e.g. AWS, Databricks, Redshift, Data Lakes).
• Proficiency in big data technologies (e.g. Hadoop, Spark, Kafka, Flink).
• Strong programming skills in Python, Scala, or Java.
• Experience with CI/CD and DataOps practices (e.g. SHIP-HATS or equivalent).
• Expertise in data integration techniques (e.g. ETL/ELT, streaming, APIs).

AI & Machine Learning (Advantageous)

• Understanding of machine learning and LLM concepts, including prompt engineering and AI application development.
• Familiarity with MLOps practices and cloud-based ML deployment.
• Awareness of responsible AI principles (fairness, robustness, safety).

Role Summary:

We are looking for a skilled Data Engineer to design, build, and maintain scalable, cloud-native data platforms. The ideal candidate has strong programming and SQL skills, hands-on experience with AWS, and a solid understanding of data modelling, ETL pipelines, and big data processing frameworks.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines and data platforms.
• Build and optimize ETL/ELT workflows using PySpark, Pandas, and SQL.
• Work with large datasets using distributed processing frameworks such as Apache Spark.
• Design and implement data models to support analytics and reporting use cases.
• Develop and manage data solutions on AWS cloud platforms.
• Orchestrate data workflows using tools such as Apache Airflow.
• Collaborate with cross-functional teams to deliver reliable, secure, and high-quality data solutions.
• Implement best practices for data quality, performance, security, and governance.
• Support CI/CD pipelines and automate deployments using DevOps best practices.

Requirements:

• 3+ years of professional experience in Data Engineering or related roles.
• At least 2 years of hands-on experience with cloud-native data services.
• Strong proficiency in one programming language: Python, Java, or .NET.
• Advanced SQL skills with experience writing complex and optimized queries.
• Strong understanding of data modelling concepts (e.g., dimensional modeling).
• Experience with relational databases (PostgreSQL, MySQL).
• Experience with NoSQL databases (DynamoDB, CosmosDB) and data lakes.
• Hands-on experience with distributed data processing frameworks (Apache Spark / PySpark).
• Experience with workflow orchestration tools such as Airflow.
• Familiarity with CI/CD pipelines and Infrastructure as Code tools.
• Experience using Git, Docker, and Terraform is a plus.

Preferred Skills:

• Experience designing end-to-end data architectures in AWS.
• Strong understanding of system design, scalability, and security best practices.
• Good communication skills and ability to work in cross-functional teams.

Job Summary:

We are looking for a senior hands-on AI/ML engineer to augment the team for the next 6 months.

• Build and scale production-grade full-stack GenAI solutions with a strong emphasis on backend systems, data, and AI/ML engineering.  
• Operate as a true partner to Business leaders - co-framing problems, influencing solution strategy, and co-owning measurable user and business outcomes - not as an execution-only engineering function.  

Job Responsibilities:

Product & Business Partnership  

• Co-own problem discovery with Product / Business: translate objectives into well defined, testable problem statements, user journeys, and technical hypotheses.  
• Challenge assumptions and requirements using user insights, data realities, feasibility constraints, and system impact; recommend alternatives when they better achieve outcomes.  
• Define and track success metrics (e.g., adoption, time saved, conversion, accuracy / quality, latency, reliability, cost-to-serve, risk reduction) and drive iteration when targets aren’t met.  
• Communicate clearly with executive, business, and engineering audiences - articulating trade-offs, decisions, and rationale.  

Solution Leadership

• Lead end-to-end solution for GenAI products: APIs, services, data pipelines, orchestration, LLM integration, retrieval, tool-calling, and UI/UX touchpoints as needed.  
• Make disciplined architectural trade-offs across performance, reliability, cost, extensibility, maintainability, and time-to-value.  
• Design for enterprise realities: identity / access, data residency, compliance constraints, multi-environment deployments, and integration with core systems.  
• Drive platform thinking: prioritize reusable components (libraries, templates, shared services) over one-off builds.  

Agentic & Full-Stack GenAI Applications

• Build and scale agentic GenAI applications that solve multi-step workflows on cloud platforms such as AWS and Azure.  
• Define and implement robust LLM patterns (prompting, RAG, tool orchestration, validation, and evaluation).  
• Implement LLMOps and lifecycle governance for models and prompts, including versioning, evaluation and monitoring.  
• Ensure solutions meet enterprise standards for quality, reliability, latency, and cost.  
• Embed Responsible AI, security, and compliance by design, partnering with Risk, Legal, and Security.  

Engineering Excellence & DevOps

• Provide hands-on leadership across the SDLC: design reviews, coding standards, testing strategy, code reviews, security reviews, and operational readiness.  
• Establish robust CI/CD and standardized environments for services and AI components utilizing cloud services.
• Ensure operational excellence through observability, incident management, reliability practices, and cost optimization.

Job Requirements:

• Proven track record in designing and building GenAI solutions, with a strong expertise in prompt engineering, LlamaIndex, Langchain, RAG, Agentic AI workflow,
• Knowledge graph, AI red teaming libraries, LLM monitoring and evaluation.  
• Over 7+ years of experience in the software development lifecycle as a developer, with a focus on writing production code.  
• Must have hands-on experience working with software development toolkits, and DevOps automation like Kubernetes, Airflow, Jenkins, Jira, Confluence and Git.  
• Excellent programming skills in Python and proficiency in PySpark.  
• Experience in Databricks, AWS bedrock, Microsoft Copilot Studio and Azure ML.  
• Strong leadership skill with ability to influence the thought process and drive alignment.  
• Excellent communication skills and a demonstrated ability to collaborate effectively with cross-functional teams.  
• Self-motivated and highly driven individual who can thrive in ambiguous requirements.

Job Summary:

We are seeking a highly skilled Big Data Platform Engineer to design, build, and operate large-scale, distributed data platforms. This role is responsible for ensuring the reliability, scalability, and performance of enterprise data systems, while driving automation, DevSecOps practices, and continuous improvement across data pipelines and platform infrastructure.

You will work closely with cross-functional teams to deliver robust, secure, and high-performing data solutions that support enterprise reporting, analytics, and data science initiatives.

Job Responsibilities:

• You are operating Global Data Platform components (VM Servers, Kubernetes, Kafka) and applications (Apache stack, Collibra, Dataiku and similar)
• Implement automation of infrastructure, security components, and Continuous Integration & Continuous Delivery for optimal execution of data pipelines(ETL/ELT)
• Develop solutions to build resiliency in data pipelines with platform health checks, monitoring, and alerting mechanisms, quality, timeliness, recency, and accuracy of data delivery are improved
• Apply DevSecOps & Agile approaches to deliver the holistic and integrated solution in iterative increments
• Liaison and collaborate with enterprise security, digital engineering, and cloud operations to gain consensus on architecture solution frameworks
• Review system issues, incidents, and alerts to identify root causes and continuously implement features to improve platform performance
• Be current on the latest industry developments and technology trends to effectively lead and design new features / capabilities

Job Requirements:

• 5+ years of experience in building or designing large-scale, fault-tolerant, distributed systems
• Migration experience of storage technologies (e.g. HDFS to S3 Object Storage)
• Integration of streaming and file-based data ingestion / consumption (Kafka, Control M, AWA)
• Experience in DevOps, data pipeline development, and automation using Jenkins and Octopus (optional: Ansible, Chef, XL Release, and XL Deploy)
• Experience predominately with on-prem Big Data architecture, cloud migration experience might come handy
• Hands-on experience in integrating Data Science Workbench platforms (e.g. Dataiku)
• Experience of agile project management and methods (e.g. Scrum, SAFe)
• Supporting all analytical value streams from enterprise reporting (e.g. Tableau) to data science (incl. ML Ops)
• Hands-on working knowledge of large data solutions (for example: data lakes, delta lakes, data meshes, data lakehouses, data platforms, data streaming solutions.)
• In-depth knowledge and experience in one of more large scale distributed technologies including but not limited to: Hadoop ecosystem, Kafka, Kubernetes, Spark
• Expert in Python and Java or another static language like Scala/R, Linux/Unix scripting, Jinja templates, puppet scripts, firewall config rules setup
• VM setup and scaling (pods), K8S scaling, managing Docker with Harbor, pushing images through CI/CD
• Experience using data formats such as Apache Parquet, ORC or Avro Experience in machine learning algorithms is a plus.
• Good knowledge of German is beneficial, excellent command of English is essential
• Knowledge of financial sector and its products

Job Summary:

We are seeking a Machine Learning Platform Engineer (MLOps) to bridge the gap between data science and production systems within our ML & AI platform team. The role spans the full ML lifecycle—from development to deployment—focusing on building robust data pipelines, managing model lifecycles, and enabling scalable AI integrations. You will also drive agentic workflows to support autonomous, AI-powered solutions in collaboration with cross-functional teams.

Job Responsibilities:

• Design, develop and deploy machine learning solutions and services
• Implement end-to-end machine learning pipelines from data ingestion to training and model serving
• Operationalize LLMs, embeddings, and multi-agent systems in real-world applications
• Manage the machine learning and model lifecycle (experimentation, registry, deployment)
• Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production
• Containerize applications using Docker and orchestrate them via Kubernetes
• Build and maintain CI/CD pipelines for ML models and LLM applications
• Collaborate with data scientists to refactor research code into production-ready Python code
• Monitor model performance, data drift, and performance in production
• Assess and integrate AI solutions ensuring optimal performance and reliability
• Design and implement production grade RAG systems
• Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes
• Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions

Job Requirements:

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field
• Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
• DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration
• Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
• MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry
• Proven experience working with Large Language Models (LLMs)
• Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns
• Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
• Hands-on experience with CI/CD pipelines (GitLab, Jenkins)
• Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization
• Relevant work experience in machine learning, data science or a related field

Role Overview:

We are looking for an experienced Senior Data Engineer to strengthen our data capabilities and drive technical excellence within the team. This position will be instrumental in building scalable data systems, shaping architecture decisions, and supporting enterprise-wide data governance practices. The successful candidate will combine strong engineering expertise with leadership and collaboration skills.

Key Responsibilities:

• Build and optimise data pipelines and ETL/ELT workflows using Python and SQL

• Design data models, system architecture, and contribute to technical decisions

• Develop APIs and enable efficient data access for downstream systems

• Implement both batch and real-time data processing solutions

• Deploy and manage cloud infrastructure using Terraform (IaC)

• Build and maintain CI/CD pipelines (e.g., GitHub Actions)

• Work extensively with AWS services (e.g., S3, Glue, Lambda, RDS)

• Drive data governance, security, and privacy standards (encryption, data protection)

• Collaborate across teams and provide technical leadership

Key Skills & Experience:

• Strong SQL and advanced Python skills

• Hands-on experience with AWS and cloud-native data platforms

• Experience with Terraform and CI/CD tools

• Knowledge of big data frameworks (e.g., Spark, Kafka, Hive)

• Familiarity with Docker/Kubernetes

• Solid understanding of data governance, quality, and security

• Strong communication skills with experience mentoring teams