Data Engineering Manager

The Red Ventures Home Client Services group is looking for a Data Engineering Manager to lead a team of up to four data engineers building the data platform that powers our home services business. In this role, you'll translate business strategy into executable technical plans across a modern lakehouse stack, serve as a credible technical point of contact for external clients and internal stakeholders, and push the team toward an AI-forward way of working. You'll bring enough technical depth to coach and redirect, and enough business fluency to engage early and drive real impact.

Curious how Home fits into Red Ventures? Click here. 

This role is open to remote or hybrid candidates (East Coast preference), with hybrid being central to our New York, NY or South Charlotte area offices. 

This role is not open to visa sponsorship or transfer of visa sponsorship including those on H1-B, F-1, OPT, STEM-OPT, or TN visa, nor is it available to work corp-to-corp.

What You’ll Do:

• Lead a team of 2-4 Data Engineers with varied experience levels to design and build data pipelines from various data sources to a target data warehouse using real-time and batch data load strategies utilizing modern cloud technologies
• Serve as a people leader through servant leadership, supporting formal performance review cycles, providing regular actionable feedback, and actively investing in each engineer's career development and long-term growth
• Own the design and delivery of data platform solutions end to end: from ingestion and transformation to outgestion, scheduling, alerting, and monitoring, ensuring the platform reliably serves business needs at scale
• Build and maintain strong relationships across business, client, and technical stakeholders, translating business strategies and requirements into a forward-looking technical roadmap, holding the team accountable to delivering against it, and serving as a credible, trusted point of contact who knows when and how to push back
• Establish and enforce standards for data governance, documentation, data quality, and pipeline observability, including lineage, access control, metadata, and end-to-end integration workflows
• Drive operational excellence across the platform: reliability, scalability, performance, cost optimization, and proactive monitoring and alerting practices
• Lead and execute proof-of-concepts where appropriate to assess, validate, and improve technical processes and approaches
• Spot and champion opportunities to build AI into the data stack, push the team toward agentic tooling and AI-assisted pipeline development, and model an AI-forward approach to how the work gets done