Lead Data Engineer

Lead Data Engineer

Agile Engine 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. If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

We are looking for a Lead Data Engineer 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.

The role requires a high degree of autonomy: you will often work on ad hoc or under specified problems, defining the problem, gathering context, identifying constraints, and shaping the right technical approach before implementation.

Design and own ETL pipelines that extract, transform, and validate data from internal databases and external APIs e architectural calls on partitioning, file formats, schema/data-type strategy, and near-real-time processing for large-volume, OLAP-oriented data systems built on an object-storage data lake. Own the design of scheduled batch workflows (DAGs) on the Airflow setup — defining pipeline structure, dependencies, and triggering strategy, and driving architectural conversations about them.

Not responsible for administering Airflow itself. Drive use of the AWS data stack (S3-backed data lake, Athena, EKS/Kubernetes), and partner directly with the Dev Ops team to clarify functional and non-functional requirements. Review PRs and enforce code quality standards. Guide senior developers and ensure alignment with established engineering practices.

MUST HAVES: 7+ years of engineering experience, with a proven track record designing and implementing ETL pipelines and making architectural decisions for large-volume data systems. Hands-on experience with OLAP-style analytical data architecture — comparable experience with Athena, Trino/Presto, Big Query, Snowflake, Spark SQL, Click House, or similar is acceptable; a specific stack isn't mandatory as long as the OLAP depth is real.

Object-storage-backed data lakes: hands-on experience designing against a data lake sitting on object storage (S3 or equivalent) queried via a serverless engine — including partitioning strategy, file formats (Parquet/ORC), and the cost/performance tradeoffs that come with them. Athena specifically is a plus, not a requirement. Task orchestration:
Deep familiarity with DAG-style workflow definition and triggering. Most batch processing is orchestrated 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.

Practical comfort across the AWS data stack — S3-backed data lake, serverless query engines (Athena or equivalent), and EKS/Kubernetes — with the ability to drive infrastructure conversations with Dev Ops. Backend proficiency in Python (FastAPI or Flask). Comfortable with REST and GraphQL. Docker and PostgreSQL for the transactional/application layer. Highly comfortable in Mac/Linux terminal-centric environments. Practical, hands-on use of AI-assisted development tools (e.g., Claude Code), 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).

Strong soft skills: the ability to hold and defend a technical opinion —…