Senior Data Analyst | Python + ETL + PySpark
Location: Ahmedabad
Experience: 4+ Years
Work Mode: Work From Office
Salary: Up to 11 LPA
About the Role:
We are looking for a Senior Data Analyst with strong expertise in Python, ETL development, SQL, and PySpark to drive data-driven decision-making across business functions. The ideal candidate should have hands-on experience building scalable data pipelines, automating workflows, optimising large datasets, and developing analytical solutions that support business growth.
Key Responsibilities:
- Design, develop, and maintain scalable ETL pipelines and automated data workflows using Python, SQL, PySpark, and MongoDB.
- Analyse large and complex datasets to generate actionable business insights.
- Build and optimise analytical data models, data marts, and reporting layers.
- Develop automated MIS reports, dashboards, and KPI frameworks using Power BI or similar BI tools.
- Implement data quality checks, validation processes, and anomaly detection mechanisms.
- Collaborate with business, risk, operations, and leadership teams to translate business requirements into scalable data solutions.
- Optimise SQL queries and data processing workflows to improve efficiency and performance.
- Drive end-to-end ownership of analytics projects from requirement gathering to deployment and monitoring.
Required Skills:
- 5+ years of hands-on experience in Python for data analysis, automation, and ETL development.
- Strong expertise in SQL, query optimisation, and performance tuning.
- 2+ years of experience with PySpark for large-scale data processing.
- Experience working with MongoDB and large datasets.
- Strong understanding of ETL processes, data warehousing, and data modelling.
- Experience with Power BI or similar reporting and visualisation tools.
- Excellent analytical, problem-solving, and stakeholder management skills.
Preferred Skills:
- Experience in Financial Services, Lending, Risk Analytics, or Fintech domains.
- Exposure to cloud platforms and modern data architectures.
- Knowledge of data governance, data quality frameworks, and automation best practices.
