SAP Technical Lead
Act as the S4 technical lead to facilitate site readiness, UAT testing and user onboarding to S4, and future deployment of critical S4 solutions
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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.
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 government or 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
Title: AWS Data Engineer
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
Location: Singapore
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
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 applicable to government/public-sector environments
About Cognizant:
Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how at www.cognizant.com or @cognizant.
Cognizant is a global IT services and consulting company whose engineers, consultants, researchers and innovators turn ideas into AI and advanced technologies for client organizations. It serves a wide range of industries including automotive, banking, consumer goods, communications and media, healthcare, insurance, retail, manufacturing, and travel and hospitality.
Its work spans areas such as data and AI, digital engineering, business processes, and consulting, helping leading organizations convert ideas into action. The company describes a supportive culture focused on continuous learning and career growth.
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