Build scalable, cost-effective ETL/ELT pipelines with AWS Glue, Athena, Redshift, S3, and Lambda (Python).
Integrate data from diverse source systems (REST, streaming, databases, SaaS) and standardize into business-specific target models (dimensional, lakehouse, or graph schemas).
Leverage orchestration frameworks (Apache Airflow, AWS Step Functions) and versioned SQL/Python transformations (dbt or similar) to enable repeatable deployments.
Implement data lineage and catalog solutions (DataHub, Amundsen, or OpenMetadata) ensuring end-to-end traceability.
Enforce data governance and control-plane policies using AWS Lake Formation or equivalent, aligning with SOC 2, ISO 27001, GDPR, and CCPA requirements.
Champion data-quality SLAs, automated testing, and monitoring (Great Expectations, Monte Carlo, or similar).
Partner with ML Engineers and Data Scientists to provision feature stores and training datasets.
Work with Backend Engineers to design efficient APIs and streaming interfaces for real-time data access.
Document data models, publish best practices, and mentor junior engineers on modern data-engineering standards.

Proven track record of deploying AI products into customer environment
Strong grasp of core machine learning, genai, agent and graph concepts, and best practices.
Experience building a customer success team
Demonstrated self-motivation, hacking mentality, and creative problem-solving abilities.
Experience contributing thought leadership and driving innovation for AI within a customer context.

Senior Data Engineer

Key skills