Description

Key Responsibilities

• Data Pipelines: Build batch/near-real-time, metadata-driven ingestion frameworks across trading, risk, treasury, core banking, and market data systems.
• Data Modeling: Design dimensional models (star/snowflake schemas), curated marts, and consumption-ready datasets; optimize warehouse partitioning and performance.
• Cloud Platforms: Develop on AWS/Azure/GCP with Snowflake/Databricks, using scalable, distributed processing frameworks.
• Data Quality & Governance: Implement quality checks, reconciliation, lineage, and observability; ensure regulatory/security compliance, secure access, and data masking.
• Performance: Tune query performance, refresh SLAs, and storage costs via partitioning, indexing, and workload optimization.
• DevOps: Build CI/CD pipelines and automate testing, deployment, and monitoring for data engineering workflows.

Mandatory Skills:

• 6–10+ years of overall experience in data engineering, data warehousing, or cloud data platform development.
• 6+ Years of Strong hands-on experience in:
  • SQL (advanced)
  • Python / Spark / Scala
  • ETL/ELT frameworks
  • Data modeling and warehousing concepts
• 6+ Years of Experience with at least one major cloud data platform:
  • Snowflake
  • Databricks
• 6+ Years of Experience building enterprise-scale data pipelines and ingestion frameworks.
• Strong understanding of data warehouse architecture patterns.
• 6+ Years of Languages & Processing: SQL, Python, PySpark, Scala
• 6+ Years of Cloud & Warehousing: AWS, Azure, Snowflake, Databricks, Redshift, Synapse
• 6+ Years of Data Engineering Tools: Airflow, dbt, Kafka, Informatica, Talend
• 6+ Years of DevOps & Automation: Git, CI/CD, Terraform, Docker, Kubernetes
• 6+ Years of Data Governance: Lineage, Metadata, Data Quality, Data Reconciliation

 Nice to Have (But not a must):

• The ideal candidate will have strong expertise in cloud data platforms, ETL/ELT engineering, data modeling, and enterprise data integration, with experience working in regulated financial services environments. The role requires close collaboration with architecture, analytics, risk, finance, operations, and application teams to modernize the enterprise data ecosystem.
• Experience in Banking / Financial Services / Capital Markets.
• Experience with:
  • Airflow / Control-M / cloud orchestration
  • Kafka or streaming platforms
  • Terraform / CloudFormation
  • GitHub Actions / Jenkins / CI-CD tools
• Exposure to lakehouse and modern data mesh architectures.

Degree: Bachelors in Computer Science or equivalent work experience

Key Skills

Education

Bachelor's degree