Senior Data Warehouse Engineer

Overview

Lead FairMoney's data warehouse, setting its architecture, modelling and quality standards and delivery roadmap, while leading and growing a team of data engineers.

What you'll do

• Own the architecture of the cloud data warehouse, from ingestion to the published datasets used by analysts, BI tools and downstream systems.
• Lead the modernisation of the data platform, including the move to CDC and streaming ingestion.
• Decide which layer each piece of business logic belongs in, and keep published datasets stable for the teams that use them.
• Manage compute and storage cost and deliver measurable efficiency gains.
• Set and enforce standards for data modelling, transformation patterns, testing, documentation and code review.
• Build data quality and observability practices that catch issues before consumers find them.
• Run a clear change process, with design review, QC evidence and peer review, before changes reach production.
• Drive automation and CI/CD across the data stack.
• Own freshness, accuracy and availability SLAs for business-critical datasets, and the on-call and incident response behind them.
• Guide the team through complex issues in distributed data pipelines.
• Lead, hire, coach and grow a team of data engineers.
• Set the team's working rhythm: intake and triage, sprint planning, design reviews and stakeholder reporting.
• Be the main engineering partner to business, product, risk and finance leaders.
• Turn business requirements into reliable data products, explain trade-offs clearly and deliver on predictable timelines.
• Make sure data handling meets financial regulatory, governance and security requirements.
• Prepare the warehouse for AI use cases with documented models, consistent metric definitions and a semantic layer.
• Bring AI-assisted engineering and analytics into the team's daily work.

What you'll need

• 10+ years in data engineering, data warehousing or analytics engineering, including 3+ years directly managing engineers.
• Expert SQL and strong Python.
• Deep data modelling experience: dimensional modelling, slowly changing dimensions, and incremental and idempotent transformations.
• Hands-on experience with a modern cloud data warehouse such as BigQuery, Snowflake or Redshift.
• Production experience with dbt and a workflow orchestrator such as Airflow.
• End-to-end ownership of business-critical datasets, with accountability for freshness, correctness and SLAs.
• Experience in fintech, banking or financial services.
• A record of hiring, retaining and growing strong engineers.
• Strong stakeholder management, including the ability to say no and explain why.

Nice to have

• Experience working with Google Cloud Platform.
• Experience working with CDC or streaming ingestion (Kafka, Debezium or similar).
• Experience with data governance, data contracts and lineage.
• In-depth knowledge of data quality and observability tooling.
• Semantic layers and GenAI / LLM tooling on structured data.
• Leading teams across time zones.

Details

• Locations: Bengaluru, Karnataka, India; Delhi, Delhi, India.
• Recruitment process: a 30-minute screening call with the Recruiter, then a 45-minute technical interview with the Hiring Manager.