– Data Engineer

Experience: 3–7 years

About CreditSea

CreditSea is an RBI-registered, digital-first NBFC operating at scale. Every disbursal, repayment, collection activity and credit decision generates data across our lending and business systems. We are strengthening our data infrastructure to build a reliable, scalable and well-governed data platform that enables better decision-making across Credit, Risk, Collections, Finance, Product, Marketing and Operations.

The Role

We are looking for a hands-on Data Engineer who can independently own and strengthen our data engineering infrastructure. The immediate focus is to

- Improve data pipelines: scalability and freshness
- Build a robust data transformation layer using DBT
- Enable easy, self-serve access to trusted data across business functions.

This is an individual contributor role with significant ownership and an opportunity to build the data engineering team as the business scales.

Reports to: Head of Engineering

Works closely with: Data & Analytics, Product, Data Science, Credit & Risk and Engineering teams.

Key Responsibilities

1. Data Pipelines & Infrastructure

· Own and improve existing core-to-warehouse data pipelines, targeting ≥99% job success through better monitoring, failure recovery and optimisation.

· Improve data freshness from the current 15-minute refresh cycles towards near-real-time availability wherever required.

· Build and maintain scalable batch and near-real-time ingestion pipelines from core lending systems, transactional databases and third-party sources.

· Optimise warehouse performance, processing efficiency and infrastructure costs as data volumes grow.

2. Data Transformation & Modelling

· Set up and own the DBT-based transformation layer,



including data models, dependencies, incremental processing, testing and documentation.

· Establish structured, reusable and well-documented data models for key business metrics across disbursals, AUM, collections, DPD, portfolio performance and other business functions.

· Implement automated data quality checks, reconciliation and validation mechanisms to ensure consistency and reliability of data.

3. Self-Serve Analytics & Data Integration

· Enable business and analytics teams to independently query data through tools such as Redash and create temporary tables or datasets for reporting and analysis.

· Build standardised mechanisms to ingest external data sources, including static Excel/CSV files and dynamic Google Sheets or other shared spreadsheets.

· Work with Analytics, Risk, Finance, Collections and other teams to understand their data requirements and make reliable datasets easily accessible.

· Reduce manual data preparation and dependency on engineering for routine reporting requirements.

4. Monitoring, Governance & Engineering Practices

· Establish pipeline monitoring, alerting, logging and SLAs for critical data workflows.

· Ensure appropriate data access controls, PII masking, security and audit trails.

· Maintain data documentation and lineage to make datasets discoverable and trustworthy.

· Follow good engineering practices around code quality,



version control, testing, deployment and production support.

- Understanding of data modelling concepts, including dimensional modelling, incremental processing and layered data architecture.
- Experience with workflow orchestration tools such as Airflow, Dagster or AWS Step Functions.
- Familiarity with cloud data infrastructure, preferably AWS (S3, Glue, Redshift, Athena or equivalent).
- Experience with pipeline monitoring, data quality checks, failure handling and performance optimisation.
- Ability to work independently, take end-to-end ownership and build systems pragmatically without overengineering.

Positive to Have

- Prior experience in a fintech, NBFC, bank or lending business.
- Familiarity with loan management systems, disbursals, repayments, collections, DPD/NPA metrics and credit bureau data.
- Experience with CDC, event streaming and near-real-time data processing (Kafka, Debezium or equivalent).
- Exposure to distributed processing frameworks such as Spark/PySpark.
- Experience with BI tools such as Redash, Metabase, Superset or Power BI.
- Familiarity with data governance, catalogue and lineage tools.
- Experience building data pipelines supporting credit risk, fraud or machine learning models.

Why Join CreditSea?

- Build and own: Take end-to-end ownership of the data engineering layer and shape how data infrastructure evolves.
- Business impact: Work on data that directly powers lending, credit, risk and collections decisions.
- Ownership and visibility: Work closely with the Head of Engineering and business leadership.
- Opportunity to grow: Play an important role in building the data function as the organisation scales.

📌 Data Engineer (Raipur)
🏢 CreditSea
📍 Raipur