Data Engineer - Snowflake
Summary
Build and scale a modern enterprise data platform using Snowflake, SQL, Python, and Power BI to power analytics, reporting, and AI workflows for a global fintech firm.
Experience
2-5 years of experience in data engineering, data science, or a related technical field.
Job Summary
An experienced Data Engineering is sought to build and scale a modern enterprise data platform. This platform will support Data Platform Services across various business functions, including Portfolio Management, Trading, Risk, Compliance, Middle Office Operations, and Finance. This position is part of a globally distributed Data Platform Engineering team (with offices in NYC and Mumbai), working in a fast-paced, agile environment with a strong emphasis on delivery.
Key Responsibilities
- Design and build robust, scalable data pipelines and architectures to support the enterprise data platform
- Develop multi-layered database structures with increasing refinement to ensure efficient data management and analytics
- Deliver curated, semantic data products that power reporting, analytics, and API-driven consumption
- Enable AI/agent workflows on top of trusted datasets, driving business value through data-driven insights
- Analyze and improve existing datasets and pipelines for data quality, performance, and reliability
- Participate in production support and on-call rotation
Requirements
- Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, or a related field
- Certifications in Snowflake (e.g., SnowPro Core), Microsoft Power BI (e.g., PL-300), or cloud data platforms (e.g., Azure DP-203) are a plus
- Senior-level expertise in building end-to-end data pipelines and semantic data models (SQL, Snowflake, Power BI, dbt)
- Strong SQL and Python engineering skills in production environments; Python (nice to have) for pipeline automation and data quality scripting
- Experience with Snowflake (schema design, clustering, Snowpark) and Power BI (DAX, semantic models, RLS, Power Query) for modern data warehousing and reporting
- Deep understanding of data flow architecture and event-driven patterns
- Proven performance tuning skills (query optimization, clustering, warehouse sizing, etc.)
- Experience with near real-time data needs at scale and high-expectation environments