Manager, Data Engineering
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The Manager, Data Engineering, is responsible for turning data architecture vision into reliable, repeatable, and scalable data pipelines and data products as part of Skillable ’s enterprise data platform. Manage a team of at least five employees and be hands-on in technical ownership of the data engineering function. Lead day-to-day engineering execution while setting strong data engineering practices across ingestion, transformation, testing, deployment, and operational support.
What You Will Do:
- Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
- Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
- Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s,timelyfeedback, development plans, and formal review cycles; andbeing responsible foroverall team performance and cohesion.
- Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
- Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behaviorleveragingBoomi,Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, andSqlServer.
- Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
- Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
- EstablishDataOpspractices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
- Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
- Drive stakeholder partnership and intake stream: collaborate with business partners to clarifyrequirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
- Develop future leaders and succession plans for key roles throughassigningregularlyoccurringresponsibilities, mentoring, and strategic delegation.
- Support and promote the company values through positive interactions with both int