Role Overview
We are looking for a highly analytical and technically strong
Senior Data Analyst with deep expertise in Python, SQL, and modern data infrastructure.
In this role, you will play a critical part in shaping and strengthening our data-driven credit strategy within the lending business. You will partner closely with Credit, Product, and Engineering teams to ensure that financial data is accurate, accessible, and actionabledirectly influencing underwriting decisions, portfolio performance, and risk management outcomes.
Key Responsibilities
- Develop and optimize complex SQL queries to analyze lending datasets, including bureau reports, GST filings, repayment history, bank statements, and alternate financial data sources.
- Build and maintain automated Python pipelines for credit underwriting, portfolio monitoring, and risk analytics.
- Manage and query large-scale structured and semi-structured datasets using PostgreSQL, ClickHouse, and other OLAP systems.
- Design and maintain Metabase dashboards to monitor critical credit metrics such as approval rates, repayment trends, delinquency ratios, and portfolio health.
- Enhance data processing performance using columnar storage formats (e.g., Parquet, ORC) and compression techniques (e.g., Snappy, Zstandard, LZ4).
- Integrate and optimize bank statement analyzers to derive income estimation, cash flow insights, and affordability assessments.
- Collaborate cross-functionally with Credit and Product teams to validate data models and ensure data integrity across underwriting and reporting layers.
- Conduct in-depth analyses across bureau, GST, payment, and alternative datasets to enable high-quality, risk-aware lending decisions.
Qualifications & Skills
- 3+ years of experience in a Data Analyst, Business Intelligence, or similar analytical rolepreferably within fintech or lending environments.
- Strong proficiency in Python for data manipulation, scripting, and workflow automation.
- Advanced SQL expertise, including writing, optimizing, and troubleshooting complex queries.
- Hands-on experience with PostgreSQL and at least one OLAP database (e.g., ClickHouse, Redshift, BigQuery, Druid).
- Working knowledge of columnar storage formats (Parquet, ORC) and their impact on query performance and storage efficiency.
- Familiarity with data compression codecs such as Snappy, Zstandard, and LZ4.
- Practical experience with Metabase, including dashboard development, filtering, embedding, and performance optimization.
- Strong analytical thinking with the ability to translate complex financial datasets into actionable business insights.
- Excellent communication skills and the ability to collaborate effectively across cross-functional teams.
Nice to Have
- Experience with data orchestration tools such as Airflow or dbt.
- Understanding of data warehousing concepts and ETL/ELT frameworks.
- Exposure to cloud-based data platforms such as AWS, GCP, or Azure.
Skills: bank statement analysis,portfolio monitoring & risk analytics,postgresql,credit bureau,data compression,olap,credit risk,fintech,gst data analysis,python,bureau data analysis,advance sql
