Manager, Data Engineering

Role overview Lead an enterprise data engineering team and own the roadmap for platforms that support reporting, analytics, AI, and data products. This is a hands-on management role: you’ll guide architecture and delivery while coaching engineers in production pipeline practices and the responsible use of AI agents throughout development, testing, documentation, and monitoring.

Responsibilities - Lead, hire, onboard, and develop data engineers, including managing performance and career growth. - Own the technical roadmap and architecture for a data platform centered on Snowflake and orchestrated with Airflow. - Integrate AI coding and agentic tools into team workflows, with appropriate evaluation, safeguards, and human review. - Partner with analytics, data science, product, and business teams to shape reliable, governed data products. - Set standards for data quality, observability, lineage, security, and platform cost efficiency. - Improve pipelines and establish scalable practices for design reviews, incident response, documentation, and delivery reporting.

Requirements - At least seven years in data engineering, including three or more years leading or managing data engineers. - Production experience with Snowflake and Apache Airflow, including performance tuning, cost management, and orchestration at scale. - Strong SQL and Python skills, with the ability to review and contribute to pipeline code. - Professional use of AI coding assistants or agentic tools in engineering workflows. - Experience with modern ELT tools such as dbt, Fivetran, or Airbyte, or comparable products. - Knowledge of data modeling, governance, and security or compliance practices; ability to communicate technical tradeoffs in business terms. - Experience working with distributed teams across time zones. - Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.

Nice to have Experience with data platform cost governance or FinOps practices.