Data Engineer - Cognizant
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Summary
AWS Data Engineer at Cognizant building and operating end-to-end data pipelines on Databricks/Snowflake for a Singapore government data platform (SDP), covering governance (Unity Catalog, RBAC, masking), CI/CD with Terraform, and observability. Requires 5+ years data engineering, incl. 3+ years with Databricks, plus strong SQL and Python.
Title: AWS Data Engineer
Location: Singapore Key Responsibilities- Design, build, and operate end-to-end data pipelines (ingestion → transformation → serving) on Databricks and/or Snowflake, following medallion/layered architecture patterns
- Configure and administer workspaces/accounts - compute policies, resource sizing, environment setup, and access hierarchy - per the SDP reference architecture
- Implement data governance controls: catalogue and schema design, RBAC, row/column-level security, data masking, and lineage tracking
- Set up CI/CD and infrastructure-as-code for pipeline deployment and environment promotion (dev → test → prod)
- Configure monitoring, telemetry, and audit logging to meet SDP's central observability and security posture requirements
- Support UAT, integration testing, and parallel-run validation during migration and go-live
- Produce handover documentation (runbooks, access lists, escalation procedures) for agency operations teams
- Unity Catalog - catalogue/schema design, access control, and data lineage
- Lakeflow / Delta Live Tables for pipeline orchestration; Delta Lake table format
- Databricks SQL and cluster/workspace administration (compute policies, pools, cost management)
- Databricks Asset Bundles (DABs) and Databricks Repos for CI/CD
- PySpark / Spark SQL for large-scale data transformation
- Working knowledge of Databricks system tables (audit logs, billing/usage, query history) for observability
- Minimum 5 years of hands-on experience in Data Engineering, Data Platform Engineering, or related disciplines.
- Minimum 3 years of hands-on experience with Databricks involving data pipeline development, platform administration, governance, and optimization.
- Strong SQL and Python (PySpark or general-purpose) for data engineering
- Data modeling - dimensional design, star/snowflake schemas, semantic layers
- CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, GitLab CI) for data engineering workflows
- Infrastructure-as-code (Terraform preferred) for provisioning cloud data platform resources
- Hands-on experience on at least one hyperscaler - AWS, Azure, or Google Cloud
- Understanding of data security and compliance frameworks
- Databricks Certified Data Engineer Associate/Professional
- SnowPro Core, or SnowPro Advanced: Data Engineer
- AWS certification (e.g. AWS Certified Data Engineer - Associate, Solutions Architect) and hands-on experience with AWS data services.
- Prior experience delivering on a regulated-sector data platform, or exposure to compliance frameworks such as IM8, is an add-on
- Experience working as part of a System Integrator (SI) delivery team alongside a platform Principal is an add-on