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Tech Stack
Tools & technologiesCloudETLNumpyPandasPythonSQL
About the role
Key responsibilities & impact- Own design and development of enterprise-scale Data Engineering and AI solutions
- Design, build, and maintain scalable and efficient data pipelines to ingest, process, and store large volumes of data
- Creating and optimizing robust data models and architectures that support advanced analytics, reporting, and machine learning initiatives
- Write production-grade SQL and Python scripts for data transformation, pipeline automation, and integration with upstream and downstream systems
- Instrument data pipelines with robust quality frameworks
- Contribute to AI integration workstreams, including building data tables and pipeline structures that support LLM-generated insight delivery
Requirements
What you’ll need- 4+ years of professional experience in data engineering or analytics engineering
- Expert-level SQL, including window functions, recursive CTEs, complex multi-level aggregations, and query performance profiling in a cloud data warehouse environment
- Intermediate Python proficiency for data pipeline scripting, ETL/ELT automation, and lightweight data wrangling using pandas, numpy, or equivalent libraries
- Demonstrated experience designing data architecture that supports analytical reporting at enterprise scale—including dimensional modeling, object rationalization, and parametric configuration layer design.
Benefits
Comp & perks- Flexible working arrangements
- Professional development opportunities
ATS Keywords
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Hard Skills & Tools
SQLPythondata engineeringdata analyticsdata modelingETLELTdata pipeline automationdimensional modelingdata architecture