AWS Data Engineer

Cognizant; Singapore; Presencial; SGD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Cognizant is seeking an AWS Data Engineer in Singapore to design, build, and operate end-to-end data pipelines on Databricks and Snowflake, following medallion patterns.

You will configure workspaces, govern data with RBAC and masking, set up CI/CD and IaC, and ensure observability, security, and compliant migration activities across environments.

Formación

• Strong SQL and Python (PySpark) for data engineering.
• Data modeling with star/snowflake schemas and semantic layers.
• CI/CD pipelines for data workflows (Azure DevOps, GitHub Actions, GitLab CI).
• Infrastructure-as-code (Terraform preferred) for cloud resources.
• Hands-on experience on at least one hyperscaler (AWS, Azure, or Google Cloud).
• Understanding of data security and compliance frameworks for government/public-sector environments.

Responsabilidades

• Design, build, and operate end-to-end data pipelines (ingestion → transformation → serving).
• Configure and administer workspaces/accounts with compute policies and access control.
• Implement data governance: cataloging, RBAC, data masking, lineage tracking.
• Set up CI/CD and IaC for deployments (dev → test → prod).
• Configure monitoring, observability, and audit logging per security posture.
• Support UAT, integration testing, and migration validation during go-live.
• Produce handover docs for agency operations teams.

Conocimientos

SQL and Python (PySpark)

Data modeling

CI/CD pipelines

Infrastructure as code

Cloud platforms (AWS/Azure/Google)

Data security & compliance

Herramientas

Unity Catalog / data lineage

Lakeflow / Delta Live Tables

Delta Lake

Databricks SQL / cluster administration

Databricks Asset Bundles & Repos

PySpark / Spark SQL

Descripción del empleo

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

Required Technical Skills — Databricks

• 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.

Required Technical Skills — Common

• 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

Preferred Qualifications

• 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

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.

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