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# Data Engineering Manager

- **Company:** Red Ventures
- **Location:** Charlotte, NC, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $130,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Information Engineering, Data Governance, Data Warehousing, Distributed Systems, Github, Python (Programming Language), Metadata, E2e Testing, Workflow Management Systems, Google Cloud, Data Lakes, Pyspark, Performance Monitor, Software Coding, Data Pipelines, Databricks
- **Published:** September 27, 2026
- **Apply:** https://www.dice.com/job-detail/92293f4b-47f3-44db-a551-d95962849540

## About the Role

* 3+ years managing a data engineering team, plus at least 5 years of hands-on data engineering experience before or alongside that management experience
 * 4+ years of hands-on Databricks experience, including Unity Catalog, Delta Lake, and workflow orchestration, with the ability to architect solutions within the platform
 * 4+ years working on cloud technologies: AWS preferred, Azure or Google Cloud Platform experience welcome
 * Strong hands-on coding experience with Python and PySpark in a distributed computing context
 * Strong background in code-first data transformation workflows, scalable data modeling, and data quality best practices
 * Deep expertise in data governance, including data quality standards, lineage, access control, and documentation, and a track record of holding a team accountable to those standards
 * Experience with pipeline observability and monitoring practices: knows what it takes to run reliable data products in production, including alerting, backfill management, and failure triage
 * Experience with GitHub and CI/CD processes in a collaborative engineering environment
 * Excellent communication skills up, down, and across the organization, with the ability to translate complex technical concepts for both technical and non-technical audiences and a proven track record of doing so in client-facing or external partnership contexts
 * Fluency in how AI is reshaping data engineering, demonstrated comfort with agentic tooling, and a track record of building AI into the stack and approaching the work differently
 * Consistently engages data engineering early in the process, understands how data is used and created across the organization, and knows how to activate a strong existing team to deliver more

## Description

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., * 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
 * Engage early with business stakeholders to understand how data is used and created across the organization, surface opportunities to deliver more value upstream, and keep data engineering involved before requirements are locked


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