Senior Data and Analytics Engineer
Pure StorageBangalore, India
Full Time
Posted November 13, 2025
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Job Description
A senior finance data analytics engineer responsible for designing and developing financial data models and infrastructure to provide enterprise-wide insights into key financial metrics, leveraging AI and modern data tools to support decision-making and financial analysis.
Key Responsibilities
- Design, build, and own the Finance Data Cube integrating financial, customer, and operational datasets
- Model data to support KPIs such as Customer Lifetime Value and Contribution Margin across multiple dimensions
- Serve as a data architect for Finance to consolidate data and answer critical business questions
- Leverage AI tools to automate data summarization, anomaly detection, and generate insights for Finance stakeholders
- Scale data infrastructure to handle large volumes and complex financial models
- Translate complex financial concepts into scalable, maintainable data models
- Implement data quality, lineage, and governance controls
- Maintain documentation of data transformations, metric definitions, and AI model logic
Requirements
- 8 to 12 years of experience in Finance Data Engineering, FP&A Analytics, or Financial Systems, with exposure to AI/ML-based analytics as an added advantage
- Proficiency with Snowflake and modern data stacks with strong SQL and data modeling expertise
- Deep understanding of finance datasets, SAP, Contribution Margin, and CLTV; experience with these would be an added advantage
- Experience with data orchestration tools such as Airflow and dbt
- Hands-on experience with Python, PySpark, or R for automation and model integration
- Proven ability to translate financial logic into scalable data models
- Knowledge of predictive modeling, time-series forecasting, and generative AI in analytics
- Ability to evaluate and apply AI responsibly in financial contexts
- Experience in designing, building, and owning a Finance Data Cube by integrating financial, customer, and operational datasets
- Ability to model data supporting CLTV, Contribution Margin, and other profitability KPIs across multiple dimensions such as customer, product, LOB, and geography
- Experience in scaling data infrastructure to support large volumes and complex financial models
- Implementing strong controls for data quality, lineage, and governance
- Maintaining clear documentation of data transformations, metric definitions, and AI model logic
Benefits & Perks
Flexible time off
Wellness resources
Company-sponsored team events
In-office work environment
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