Data Engineering Manager

Role Overview

We are seeking an experienced Data Engineering Manager to lead and scale our data engineering team and platform. This role blends people leadership, technical oversight, agile delivery, and cross-functional partnership to enable reliable analytics, BI, and AI/ML workflows across the organization. You will manage an established team of engineers and drive execution of a modern data stack built on Snowflake, DBT, and AWS, supporting primarily batch processing with limited streaming sources.

Key Responsibilities

• Lead, mentor, and develop a team of data engineers; support hiring and team growth
• Own delivery of the data engineering roadmap, ensuring predictable execution through agile practices (sprint planning, prioritization, backlog management)
• Partner cross-functionally with Analytics, BI, Data Governance, Product, and Technology teams to translate business needs into scalable data solutions
• Oversee the design, reliability, and performance of data pipelines and models supporting Quicksuite and Looker
• Establish best practices for data quality, testing, monitoring, and operational excellence
• Communicate progress, risks, and technical concepts clearly to stakeholders and executive leadership
• Enable high-quality data foundations for emerging AI/ML use cases

Qualifications

• 7+ years of experience as a Data Engineer
• 5+ years in a lead or managerial role within data engineering
• Experience with tools such as DVP, Daxter, etc. that use Airflow as their module.
• Proven success scaling teams and delivering data initiatives in growing organizations
• Strong expertise with Snowflake, DBT, and AWS
• Deep understanding of batch processing architectures; familiarity with streaming is a plus
• Experience supporting BI and analytics platforms (Looker or similar)
• Strong project management, agile delivery, and stakeholder communication skills
• Plus: Experience with DataQuality, including data profiling and observability, and data governance

Success in This Role Looks Like

• A high-performing, growing engineering team with clear delivery processes
• Trusted partnerships across business and technology stakeholders
• Reliable, scalable data pipelines that accelerate analytics and AI initiatives
• Strong communication and execution aligned to organizational priorities