Data Engineer with Databricks, SQL

About the Role: The Data Foundations team is looking for a Data Engineer ready to own meaningful work end-to-end. You’ll design and deliver production-grade data pipelines, ensure the data your team ships is trustworthy and well-defined, and actively use AI tools like Claude to move faster and build smarter. You bring solid engineering fundamentals and an AI-first mindset to everything you build. Key Responsibilities Design & Build Production Pipelines: Architect and implement ELT/ETL pipelines using Databricks and dbt; build robust data workflows on Azure (ADLS, ADF) to support batch and near-real-time use cases. Own Data Quality & Reliability: Take end-to-end ownership of data quality in your domain define schemas, freshness SLAs, and ownership boundaries so that downstream consumers, including the Reporting team, can depend on the data you produce. Use AI to Build Better, Faster: Actively use Claude to generate code, automate repetitive engineering tasks, prototype new solutions, and accelerate delivery while maintaining full ownership and accountability for what ships. Write Maintainable, Tested Code: Write clean, testable Python and SQL; contribute to CI/CD pipelines in Azure DevOps; participate in and lead code reviews with a focus on quality and long-term maintainability. Tune for Performance & Cost: Profile and optimize Databricks notebooks, dbt models, and Snowflake queries to meet SLAs without unnecessary cloud spend. Work Closely with the Reporting Team: Partner with the Reporting team and product managers to understand downstream data needs and deliver data products that directly support business decisions for Growth and Emerging Business. Deliver in Agile Sprints: Operate effectively within Scrum own your sprint commitments, participate in planning and retrospectives, and communicate blockers and progress transparently. Operate & Improve Production Systems: Serve as a primary on-call responder for pipelines in your domain; own incident triage and resolution; write post-incident summaries and translate learnings into platform improvements.

Required Qualifications Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience. 2-4 years of experience in data engineering or a closely related software engineering role. Strong SQL and Python skills applied to data transformation and pipeline development. Hands-on experience with Databricks (Spark, Delta Lake) and Snowflake. Experience building pipelines on Azure (ADF, ADLS, or equivalent). Experience with dbt for data transformation and modeling. Solid Git workflow practices and CI/CD experience, ideally with Azure DevOps. Demonstrated use of AI tools (Claude, GitHub Copilot, or similar) to accelerate and improve your engineering work. Experience working in Agile/Scrum teams. Preferred Qualifications Familiarity with data quality frameworks such as Great Expectations or dbt tests. Experience with orchestration tools like Airflow or Azure Data Factory pipelines.