Gcp Data Engineer

TCS invites application for GCP Data Engineer
Experience Range: 6+ Years
Location: Pan India
Notice Period: Any

Job Description -

ROLE SUMMARY

Design, build and optimise cloud-native data platforms and pipelines on Google Cloud for BFSI modernisation and migration programs. The role centres on BigQuery-based data warehousing, Dataproc/Spark processing and orchestrated, cost-efficient pipelines that deliver scalable analytics and AI-ready data products for banking, financial services and insurance clients.

KEY RESPONSIBILITIES

• Strong Experience and understanding of Data warehouse, Data Lake platforms archutetcure and design aspects
• Design and develop scalable batch and streaming pipelines on GCP using Dataflow, Dataproc (Spark), Pub/Sub and Cloud Functions.
• Build and optimise cloud data warehouse solutions on BigQuery, including partitioning, clustering and query cost optimisation.
• Migrate and modernise legacy workloads (Hadoop, Teradata, on-prem ETL) to GCP using Dataproc, BigQuery and Data Fusion.
• Develop ELT/ETL workflows with Python, Java, Scala and PySpark; orchestrate pipelines using Cloud Composer (Airflow).
• Implement data security and governance via Cloud IAM, and build reusable data validation and reconciliation frameworks.
• Tune Spark and BigQuery workloads for performance and cost, achieving measurable runtime and storage improvements.
• Set up CI/CD and automation for GCP data assets using Git-based pipelines and infrastructure-as-code.
• Collaborate with architects and client teams on solutioning, PoCs, demos and production cutover.

TECHNICAL SKILLS

Core / Must-Have

• Google Cloud Platform (BigQuery, Dataproc, Dataflow)
• Pub/Sub, Cloud Functions, Data Fusion, Data Transfer
• PySpark / Spark, Python, SQL (Java/Scala a plus)
• Cloud Composer (Airflow) orchestration
• Data warehouse & pipeline design + performance tuning
• Cloud IAM data security & access governance

Complementary / Good-to-Have

• Legacy migration (Hadoop / Teradata / on-prem ETL)
• GCP Kubernetes Engine (GKE), Cloud Storage
• CI/CD, Git, infrastructure-as-code (Terraform)
• Data validation / DVT frameworks & automation
• BI & analytics Looker, Power BI, Tableau
• Exposure to Vertex AI / GenAI on GCP