Data Engineering Manager

Data Engineering Manager opportunity in the Greenville, SC area with a hybrid schedule (4 days remote, 1 day on-site) for a degreed data professional with 5+ years of technical experience, including 3+ years leading a team. In this role, you will lead a small data team and set its technical direction, while staying hands-on in Python, SQL, and data pipeline engineering. The ideal candidate is a self-directed builder who sees data systems as software that should be engineered, tested, automated, and monitored, and who enjoys developing people as much as systems. Join a well-established e-commerce and logistics company with 25+ years of experience offering a casual small-company culture with big-company stability and real autonomy to innovate.
This company offers excellent salary, benefits, bonus and 401k, and room to grow!
Requirements

• 5+ years of applicable technical experience, and 3+ years leading, mentoring, or providing technical direction
• Significant experience developing and supporting production data or software systems, including designing and building data pipelines and integrations
• Strong skills in SQL, Python (or another scripting language), DBT, Snowflake, Postgres, and SQL Server
• Experience with git version control, relational database design, and Linux/command-line environments
• Working knowledge of data warehousing, modern analytics architectures, formal data modeling, and workflow orchestration concepts (dependencies, retries, failure handling, monitoring, recovery)
• Bachelors degree in technical discipline

Responsibilities

• Lead and develop a small team spanning database administration, data engineering, and reporting and analytics
• Set technical direction: automate manual and repetitive processes, select tools, and continuously improve the tech stack
• Remain a hands-on contributor, designing, building, troubleshooting, and reviewing Python and SQL solutions, pipelines, and integrations (Snowflake, DBT, DLT, and related tools)
• Promote strong engineering practices, including data modeling, source control, automated testing, code review, infrastructure as code, and monitoring and observability
• Advance self-service analytics and governed data models to reduce reliance on manually produced reports
• Manage the team budget, including software and vendor services, and pragmatically evaluate new technologies