Lead Data Engineer ID71008

<span id="spandesc"> <div> AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards. </div> <div> </div> <div> <b> WHY JOIN US </b> </div> <div> If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! </div> <div> </div> <div> <b> ABOUT THE ROLE </b> </div> <div> We are looking for a <b> Lead Data Engineer </b> to own the data pipeline and analytical architecture layer for a large-volume marketing analytics platform. You will make architectural decisions around partitioning strategy, file formats, schema design, and near-real-time processing for OLAP-oriented workloads built on an S3-backed data lake. You will design and govern ETL pipelines, define DAG-based orchestration strategies using Airflow, drive the AWS data stack including Athena and EKS, and lead a team of senior developers while enforcing code quality standards. </div> <div> </div> <div> <b> WHAT YOU WILL DO </b> </div> <div> - Design and own ETL pipelines that extract, transform, and validate data from internal databases and external APIs at scale. </div> <div> - Make architectural decisions on partitioning strategy, file formats, schema and data-type strategy, and near-real-time processing for large-volume, OLAP-oriented data systems built on an object-storage data lake. </div> <div> - Own the design of scheduled batch workflows (DAGs) on the client's Airflow setup, defining pipeline structure, dependencies, and triggering strategy, while driving architectural discussions. Not responsible for administering Airflow itself. </div> <div> - Drive use of the client's AWS data stack (S3-backed data lake, Athena, EKS/Kubernetes), and partner directly with the client's DevOps team to clarify functional and non-functional requirements. </div> <div> - Review pull requests and enforce code quality standards. </div> <div> - Guide senior developers and ensure alignment with the client's engineering practices. </div> <div> </div> <div> <b> MUST HAVES </b> </div> <div> - <b> 7+ years of engineering experience, with a proven track record designing and implementing ETL pipelines and making architectural decisions for large-volume data systems </b> . </div> <div> - Hands-on experience with <b> OLAP-style analytical data architecture </b> . Experience with Athena, Trino/Presto, BigQuery, Snowflake, Spark SQL, ClickHouse, or similar technologies is acceptable; a specific stack isn't mandatory as long as the OLAP depth is real. </div> <div> - Hands-on experience designing against a <b> data lake sitting on object storage (S3 or equivalent) queried via a serverless engine </b> — including partitioning strategy, file formats (Parquet/ORC), and the cost/performance tradeoffs that come with them. Athena specifically is a plus, not a requirement. </div> <div> - Deep familiarity with <b> DAG-style workflow definition and triggering </b> . The client orchestrates most batch processing through Airflow, so this role needs either substantial prior Airflow experience they can draw on to drive architectural conversations, or enough depth in a comparable orchestrator (Dagster, Prefect, Luigi, Step Functions) to ramp on Airflow quickly and lead those conversations from day one. Managing the Airflow deployment itself is out of scope. </div> <div> - Practical experience across the <b> AWS data ecosystem, including S3-backed data lakes, serverless query engines such as Athena or equivalent, and EKS/Kubernetes </b> , with the ability to drive infrastructure conversations with DevOps. </div> <div> - Strong backend proficiency in <b> Python, including FastAPI or Flask </b> . </div> <div> - Comfortable working with REST and GraphQL. </div> <div> - Experience with <b> Docker and PostgreSQL </b> for the transactional and application layer. </div> <div> - Highly comfortable working in Mac/Linux terminal-centric environments. </div> <div> - Practical, hands-on use of <b> AI-assisted development tools (e.g., Claude Code) </b> , paired with the critical judgment to challenge AI output when it compromises long-term maintainability — including the leadership presence to set the standard for how the team uses AI tooling responsibly (e.g., flagging risky AI-driven shortcuts during PR review). </div> <div> - Strong soft skills: the ability to hold and defend a technical opinion — challenging a stakeholder's or a tool's proposed "quick fix" with sound reasoning in pursuit of a solution that scales and is maintainable long-term, while still being pragmatic enough to ship. </div> <div> - Upper-intermediate English level. </div> <div> </div> <div> <b>