Job Summary

- 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 solid historical snapshots, data quality and operational reliability.

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.

- B.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.

📌 Senior Data Engineer (Mumbai)
🏢 Paramount HR
📍 Mumbai