DATA ENGINEERING MANAGER

Dollar General • Goodlettsville, TN

3h • $135,000 - $145,000

About The Position

This position manages the delivery and operations of assigned data platforms, pipelines, integrations, and related capabilities. The role is accountable for the reliable, secure, governed, and cost-effective operation of the team’s services, enabling trusted data products and timely business decisions. The manager develops a high-performing team and delivers prioritized outcomes within established enterprise strategy, architecture, and engineering standards.

The manager oversees team intake, delivery planning, ongoing production support, service reliability, data-product quality, cost and capacity monitoring, and team-level improvements to engineering automation and delivery practices. The role hires, coaches, develops, and performance-manages 4–8 direct reports, and communicates delivery scope, risks, dependencies, and tradeoffs to stakeholders. Material cross-team issues and staffing or vendor needs are escalated to the Director for review.

Requirements

• Strong knowledge of data engineering, data platforms, data pipelines, databases, integrations, and modern cloud data services.
• Strong hands-on proficiency with Python, SQL, ETL/ELT, orchestration, data modeling, production operations, and data-product quality practices.
• Ability to apply engineering standards, participate in architecture and design reviews, and make sound team-level technical and delivery decisions.
• Working knowledge of data privacy, security, governance, access controls, retention, lineage, metadata, and compliance requirements, and the ability to implement applicable controls in team delivery.
• Demonstrated people leadership in hiring, coaching, performance management, and team development.
• Strong stakeholder management, prioritization, communication, analytical, and problem-solving skills, including the ability to communicate delivery tradeoffs.
• Ability to use approved AI-assisted engineering tools responsibly to improve productivity, testing, documentation, troubleshooting, and data-quality work while safeguarding company data.
• Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field, and 3–5 years of progressively responsible experience in data engineering, software engineering, analytics engineering, or related disciplines, including 1–2 years of technical leadership, team-lead, or people-management experience.
• Demonstrated experience delivering and supporting production data pipelines, integrations, or data products.
• Hands-on experience with Python, SQL, ETL/ELT, orchestration, data modeling, cloud data services, and production operations.
• Experience implementing data privacy, security, governance, quality, lineage, and access-control requirements within engineering delivery.
• An equivalent combination of education and/or experience may be considered.

Nice To Haves

• Working knowledge of Google Cloud Platform and BigQuery; experience with these is strongly preferred.
• Familiarity with Cloud Composer/Airflow, Dataflow, Pub/Sub, dbt, and Terraform is preferred.
• Experience with infrastructure-as-code is a plus.
• Experience responsibly using approved AI-assisted engineering tools to improve development, testing, documentation, and operational productivity is preferred.

Responsibilities

• Manage team intake, delivery plans, and ongoing support for assigned data platforms, pipelines, integrations, and data products; communicate scope, risks, dependencies, and tradeoffs to stakeholders and escalate material cross-team issues to the Director.
• Apply established data-engineering standards and architecture practices to team delivery; contribute implementation input to technical roadmaps, design reviews, and platform lifecycle decisions.
• Ensure reliable operation of production data services through agreed service targets, observability, incident response, production support, change management, and recovery practices; monitor cost and capacity and escalate material risks.
• Hire, coach, develop, and performance-manage 4–8 direct reports; support workforce planning and identify staffing, skill, or vendor needs for Director review.
• Apply approved data-product quality practices, including schema and data contracts, testing, reconciliation, documentation, monitoring, and discoverability; drive resolution of quality issues within the team’s services.
• Drive team-level improvement in engineering automation, tooling, and delivery practices, including responsible use of approved AI-assisted tools in accordance with security, code-review, company-data protection, and engineering standards.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager