Data Engineer

Design, build, and optimize scalable data pipelines to support Research Digitization, Banking, Global Markets, and Data Monetization use cases
Develop and operationalize data products across structured and unstructured sources, including alternative data
Deploy, manage, and performance-tune large-scale Spark workloads on Databricks, ensuring reliability, scalability, and cost-efficiency
Collaborate with data scientists, quant teams, and business stakeholders to enable data-driven decision-making
Contribute to automation efforts via CI/CD pipelines, infrastructure-as-code, and reusable data frameworks
Must Have:
Strong experience with Python and Spark (PySpark)
Hands-on with Databricks (Jobs, Workflows, Delta Lake, Unity Catalog)
Proficient in SQL for complex data transformations and optimizations
Proficient in CI/CD principles, version control and best practices for deploying workloads to production
Solid understanding of distributed data processing and production-grade data workflows
Nice to Have:
Exposure to Machine Learning workflows and tools like MLFlow
Exposure to Generative AI stack (LLMs, Agents, MCP Servers)
Familiarity with Snowflake, Airflow, or similar orchestration and warehousing platforms