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Data Engineer
Skills
Data WarehousingDatabasesDownstream Oil And GasMedical NecessityModelingSnowflake CloudStructured Finance
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 5+ years building production data systems and pipelines in Python or a comparable typed language
- Strong SQL and data-modeling fundamentals
- Experience with a modern cloud warehouse such as Snowflake BigQuery or Redshift and a transformation framework like dbt
- Hands-on experience deploying machine-learning models to production including training inference evaluation and rollout
- Familiarity with at least one transformer-based ML framework such as PyTorch and Hugging Face Transformers
- Working sense of when classical or embedding-based models beat LLMs and when they do not
- Resourceful curious and comfortable learning new tools quickly
- Thrive in fast-paced dynamic environments and enjoy wearing multiple hats
- Collaborative and enjoy working across teams to solve problems
- Execution mindset with focus on end users
- Proficient at leveraging AI tools to ship faster
Nice to have
- Resourceful
- Curious
- Collaborative
- Fast-paced
- Execution-minded
- Multi-hat
Day to day
- Design and own data pipelines and ML services that classify product eligibility and support downstream decisions at checkout
- Model the data domain for products merchants eligibility rules classifications and outcomes in warehouses and serving systems
- Partner with backend product and operations teams to turn merchant and consumer needs into reliable data products models and APIs
- Improve warehouse transformation ML training inference and real-time serving architecture while resolving data quality model accuracy and latency issues
- Build evaluation harnesses golden datasets observability documentation and on-call runbooks to support operational excellence