Job description

Job Role: SENIOR DATA ENGINEER – RISK & DATA PLATFORM

Experience: 7+ Years
Location: Mumbai (Andheri West)

About the role

• Own the design, build and scale-up of the data layer for GLAAS risk and credit systems, bringing together LOS/LMS, bureau, banking, GST, partner and offline data into reliable, reusable pipelines and stores.
• Build production-grade data foundations for underwriting, credit models, Customer 360, portfolio monitoring, EWS, reporting and audit – with strong historical snapshots, data quality and operational reliability. Key Responsibilities
• Design scalable batch, file and API-based ingestion pipelines for SQL/Mongo sources and external data, with clear contracts, schemas, refresh SLAs and ownership.
• Build curated historical datasets, point-in-time snapshots, analytical marts and OLAP-ready models for underwriting, portfolio analytics, model development and monitoring.
• Develop and operate ETL/ELT workflows, DAGs, scheduling, incremental processing, backfills and reruns using Python, SQL and modern orchestration frameworks.
• Implement reconciliation, lineage, observability, data-quality checks, exception queues, alerts and incident controls so critical risk data is complete, timely and auditable.
• Partner with Risk, Data Science, Product and Engineering to productionize model inputs, feature pipelines, APIs and BI datasets while improving performance, resilience and cloud cost.

 Core Competencies

• High ownership and execution discipline – comfortable taking ambiguous data problems from architecture through production support.
• Strong systems thinking, with attention to correctness, scalability, failure handling, security and operational simplicity.
• Clear communicator who can work effectively across Engineering, Risk, Data Science, Product, Operations and business teams.

 Must-Have Requirements

• 5-8 years of hands-on data engineering experience with strong Python and advanced SQL, including production ETL/ELT pipelines and data modelling.
• Strong experience with relational and NoSQL databases, APIs, workflow orchestration, Git-based development and cloud data infrastructure; GCP is preferred, while AWS/Azure experience is acceptable.
• Demonstrated ownership of large historical datasets, incremental loads, backfills, reconciliation/data quality controls, performance tuning and production debugging.
• Exposure to Spark/PySpark, Kafka or streaming, dbt, Docker, CI/CD, data observability and modern warehouse/lakehouse architecture.
• Understanding of ML workflows, feature stores, model input pipelines or credit-risk data such as bureau, banking, GST and repayment data.
• Tech/B.E Good-to-Have (Optional)
• Experience in fintech, NBFC, banking, embedded finance or another domain involving high-integrity transactional and decision data. Ideal Candidate Profile
• A hands-on senior engineer who can independently design architecture, write production code and investigate source-level data issues when required.
• Has built evolving data platforms in a fast-moving product environment where sources, schemas and business requirements change frequently.
• Enjoys building reusable foundations and automation, and is motivated by creating a dependable data backbone for credit and risk decisions.