Description

Build and optimize enterprise-scale data pipelines and warehousing solutions for regulated financial systems.

This role is on-site.

Responsibilities

• Develop batch and near-real-time ingestion frameworks across trading, risk, and treasury systems.
• Design dimensional data models (star/snowflake schemas) and curated marts with optimized partitioning.
• Implement data quality checks, lineage tracking, and observability to ensure regulatory compliance.
• Tune query performance, refresh SLAs, and manage storage costs through workload optimization.
• Build CI/CD pipelines to automate testing, deployment, and monitoring of data engineering workflows.

Required Skills

• 6–10+ years of experience in data engineering, data warehousing, or cloud data platform development.
• Advanced SQL, Python, PySpark, and Scala for scalable, distributed processing.
• Hands-on experience with major cloud data platforms: Snowflake, Databricks, AWS, or Azure.
• Strong understanding of data warehouse architecture patterns and enterprise-scale pipeline construction.
• Proficiency with ETL/ELT frameworks and orchestration tools like Airflow or Control-M.
• Experience with DevOps and automation tools: Git, Terraform, Docker, Kubernetes, and CI/CD.
• Knowledge of data governance, including metadata management, data quality, and reconciliation.

Preferred Skills

• Experience in Banking, Financial Services, or Capital Markets.
• Exposure to lakehouse architectures, data mesh, or streaming platforms like Kafka.

Bachelor's degree in Computer Science or equivalent work experience.

Key Skills

Education

Bachelor's degree