Sr. Data Analyst

Time Hack Consulting; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

A fintech analytics firm is seeking a Senior Data Analyst to enhance its data-driven credit strategy. This role requires strong expertise in Python and SQL, alongside hands-on experience with PostgreSQL and OLAP systems. Key responsibilities include developing SQL queries, building automated Python pipelines, and maintaining dashboards for critical credit metrics. Ideal candidates will have 3+ years in analytical roles, strong communication skills, and an analytical mindset to translate complex datasets into actionable insights. Hybrid work environment available.

Qualifications

• 3+ years of experience in a Data Analyst or similar analytical role.
• Strong proficiency in Python for data manipulation and automation.
• Advanced SQL expertise, including optimization of complex queries.

Responsibilities

• Develop and optimize SQL queries to analyze lending datasets.
• Build and maintain automated Python pipelines for credit underwriting.
• Design and maintain Metabase dashboards for monitoring critical metrics.

Skills

Python

SQL

PostgreSQL

Metabase

Data analysis

Financial insights

Data compression

OLAP systems

Tools

PostgreSQL

ClickHouse

Airflow

Job description

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 actionable—directly 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 role—preferably 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.