Senior Data Engineer

Overview

Own recurring analytics deliverables and controls, ensuring data quality, governed datasets, and production data pipelines for frequent reporting, validation, and ad hoc analysis.

What you'll do

• Own end-to-end data pipelines for Predictive Analytics workflows.
• Support monthly dashboard validation, trend and offer-lag analysis, quarterly attrition reporting, and EAR CBA performance tracking.
• Ensure data accuracy using controls, consistent definitions, and version management with data/automation partners.
• Translate business requirements into governed, reusable datasets, metric definitions, and scalable reporting assets.
• Apply analytics engineering practices using modular SQL/Python, peer review, version control, testing, and documentation.
• Maintain process documentation/runbooks and co-ownership for access continuity and escalation during failures.

What you'll need

• 3–6 years of experience in data engineering/analytics engineering/reporting/dashboarding in financial services or a data-driven environment.
• Experience building or maintaining reusable datasets or productionized analytics workflows.
• Strong experience with data validation, reconciliations, and control routines for recurring reporting and model outputs.
• Proficiency in SQL and Python.
• Working knowledge of modern data stack concepts including warehouses/lakehouses, ELT patterns, dimensional modeling, semantic layers, and data lineage.
• Ability to partner with data engineering on ingestion/orchestration/access patterns while owning analytics transformation/business logic.

Details

• Employment type: Full time.
• Work schedule: 2:00pm–10:30pm (India).
• In-office collaboration: at least three (3) days per week in office; flexible to work from home two (2) days per week.