Senior Manager, Data Engineering

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The Senior Manager, Data Engineering manages a team of data engineers who design, build, and operate the data products powering analytics and product use cases across the business. The team develops in dbt, manages source control and CI/CD in Git Lab, and deploys workloads to Kubernetes. Day to day, you will set technical direction for the team, conduct 1:1s and coaching sessions, run team standups, and provide involved technical guidance to unblock engineers.

You will keep work flowing by managing JIRA, grooming the backlog, prioritizing sprints, and making sure engineers stay aligned with their goals and OKRs.

Beyond people leadership, you will act as the technical owner for the team’s portfolio of data products. You will partner with Product Management, data consumers, and platform teams to translate business needs into a clear engineering roadmap, raise the bar on data quality and reliability, and ensure the team consistently ships against deadlines.

As a leader in the team's adoption of AI, you will stay current with the latest models, tools, and techniques, integrate them into your own day-to-day workflow, and empower the team to adopt them where they accelerate the work. You will set the bar for thoughtful, engaged AI usage in data engineering and coach engineers on how to apply it effectively.

• Manage the day-to-day operations of the data engineering team, including standups, weekly 1:1s, coaching engineers on growth and development, and providing involved technical guidance and code reviews.
• Serve as a technical lead for the team, bringing expertise in dbt, SQL, and modern data stack architecture to guide technical direction, unblock engineers, and improve engineering quality.
• Build collaborative relationships across analytics, product, operations, and engineering teams to understand data needs and drive alignment on architecture, standards, and platform decisions.
• Partner closely with Product Management to shape roadmaps, scope work, prioritize initiatives, and ensure consistent delivery against team goals and timelines.
• Define and maintain SLOs and SLAs for data products, ensuring appropriate monitoring, on-call processes, and support coverage are in place.
• Manage team execution through effective backlog grooming, sprint planning, and delivery tracking in JIRA and against OKRs.
• Stay current on emerging AI models, tools, and capabilities, regularly applying them in day-to-day work and encouraging thoughtful adoption to improve team efficiency, quality, and impact.

• Requires a Bachelor’s degree in Computer Science or Computer Engineering, and a minimum of 12 years of related experience; or a Master’s degree with a minimum of 8 years of experience; or a PhD with a minimum of 5 years experience
• 5+ years of direct experience building and maintaining production-grade transformation pipelines in dbt.
• 7+ years of experience writing complex, high-performing SQL for analytical data warehouses, including 3+ years working with Snowflake on performance tuning, warehouse sizing, and RBAC.
• 3+ years of experience working with Apache Iceberg or comparable open table formats such as Delta or Hudi, along with orchestrating production data pipelines using Apache Airflow.
• 2+ years using Jinja for reusable templated SQL and transformations, plus 2+ years of Python experience supporting data engineering automation and tooling.
• Experience with modern data engineering infrastructure and tooling, including Git Lab for source control and CI/CD and Kubernetes-based deployment of data workloads.
• Demonstrated experience leading and developing data engineering teams, including coaching, hiring, performance management, sprint execution through JIRA, and cross-functional partnership to deliver reliable data products…