Data Engineer I
- Reduced pipeline execution time by up to 60% by optimizing critical ETL/ELT workloads across AWS, Databricks, dbt, and Airflow through SQL tuning, DAG refactoring, Airbyte incremental syncs, and parallelism improvements
- Drive end-to-end delivery of high-impact data pipelines on the financial data platform, expanding scope across AWS Athena, MWAA, Airbyte, Databricks, and PostgreSQL
- Partner directly with business and product stakeholders to elicit requirements and translate them into reliable data products, using AI agents (Amazon Q Developer, Claude Code, Databricks Genie) and MCP-based tooling to accelerate requirement discovery, data exploration, and execution
- Advance DataOps and DevOps maturity through GitHub Actions CI/CD, Terraform-managed infrastructure, automated dbt tests, and SLAs on Airflow pipelines, raising platform reliability and deployment velocity
- Own cross-functional delivery within Agile (Jira, GitHub) and present technical solutions in engineering reviews, increasing visibility of data platform work across analytics, product, and DevOps partners
