Data Engineer - A26408

Activate Interactive Pte Ltd ("Activate") is a leading technology consultancy headquartered in Singapore with a presence in Malaysia and Indonesia. Our clients are empowered with quality, cost-effective, and impactful end-to-end application development, like mobile and web applications, and cloud technology that remove technology roadblocks and increase their business efficiency. We believe in positively impacting the lives of people around us and the environment we live in through the use of technology. Hence, we are committed to providing a conducive environment for all employees to realise their full potential, who in turn have the opportunity to continuously drive innovation. We are searching for our next team members to join our growing team. If you love the idea of being part of a growing company with exciting prospects in mobile and web technologies that create positive impact on people's lives, then we would love to hear from you. Co-Development Business Unit is hiring for Senior Data Engineer You will work in Singapore Government Agencies This is a fixed term contract role. The engagement is 1 year. Internal Code: A26408 What will you do? The Senior Data Engineer sits within the DSAD Platform and Architecture Team and is responsible for designing, building, productionising and operating trusted data pipelines and reusable data products on the SWDA data platform using AWS and Databricks. The role will provide technical leadership across data ingestion, transformation, modelling, quality, lineage, orchestration, observability and performance, applying CRISPr principles of comprehensiveness, robustness and scalability so that data remains dependable for analytics, reporting and ML/AI workloads. • Data Pipeline Architecture and Delivery: Lead the design and implementation of maintainable batch and streaming pipelines that ingest, validate, transform and publish structured, semi-structured and unstructured data across AWS and Databricks. • Lakehouse and Data Product Engineering: Design Bronze, Silver and Gold data layers using Delta Lake or equivalent patterns; develop reusable domain data models, curated datasets and data products with explicit ownership, definitions and service expectations. • Source Integration and Incremental Processing: Implement reliable ingestion from databases, files, APIs and event streams using full-load, incremental, change-data-capture and replayable processing patterns appropriate to each source. • Data Quality and Contracts: Establish schema enforcement, data contracts, automated validation, reconciliation and anomaly checks covering completeness, validity, consistency, uniqueness, timeliness and referential integrity. • Orchestration and Deployment Automation: Build parameterised workflows using Databricks Workflows or Lakeflow and relevant AWS services; integrate automated testing, infrastructure as code and CI/CD controls across development, test and production environments. • Metadata, Lineage and Governance: Implement technical metadata, business definitions, end-to-end lineage, classification, ownership and access controls using Unity Catalog, AWS Glue Data Catalog, Lake Formation or approved platform capabilities. • Pipeline Monitoring and Reliability: Define dashboards, alerts, logging, operational metrics and runbooks for pipeline health, freshness, data-quality failures, latency, throughput and downstream impact; lead troubleshooting and root-cause analysis. • Performance and Cost Optimisation: Optimise Spark and SQL workloads, file sizes, partitioning, clustering, caching, compaction, cluster configuration and workload scheduling to improve runtime, reliability and AWS/Databricks cost efficiency. • Security and Data Protection: Apply least-privilege IAM, encryption, secrets management, environment separation, audit logging, sensitive-data controls and secure data-sharing patterns in line with applicable agency requirements. • Technical Leadership and Quality: Set engineering standards, review designs and code, mentor engineers, manage technical debt and promote reusable components rather than project-specific pipelines. • Stakeholder Collaboration: Work with data owners, stewards, analysts, data scientists, architects, product teams and governance stakeholders to translate analytical and operational needs into implementable data specifications. • Documentation and Knowledge Transfer: Maintain architecture decisions, source-to-target mappings, data models, contracts, operating procedures and support documentation, and transfer knowledge to the internal team throughout the engagement. Requirements • Strong Python, PySpark and SQL capability, including distributed processing, complex transformations, query optimisation, debugging and automated testing. • Hands-on experience with Databricks, Apache Spark and Delta Lake for production-scale batch or streaming data engineering. • Hands-on AWS data engineering experience with a relevant mix of S3 #J-18808-Ljbffr