Manager, Data Engineering

Skillable is a 100% remote and virtual tech company that’s modernizing the world of training. Come share your professional magic with highly talented, driven and fun colleagues who believe in the power of “skilling.” Experience what a true team focused on doing the right thing feels like!Our people and talent are what make us great and fun! We work together to create amazing solutions and experiences for our customers and their clients. We utilize our employees’ personal strengths to help our company grow and ensure our team is living their best, authentic life. We don’t just share our appreciation for our team members once a year with a branded mug—it’s shared on a daily basis. Our remote work environment blends the demands of work and life without the added pressure of commuting or feeling guilty about leaving early to visit the dentist.Come work with us and learn what teamwork and integrity blended with an emphasis on well-being and balance can do for your career!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.ResponsibilitiesManage 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 internal and external stakeholders on a regular basis.Other strategic business initiatives or special cross-functional project involvement asrequired.QualificationsBachelor’s degree in Computer Science, Data Science, Engineering, or relevant professional experience.10+ years of relevant professional experience in software / data engineering, including building production data pipelines and data platforms.2+ years of experience in lead capacity (formal or informal) including team leadership, task breakdown, reviews, delivery ownership, and cross-functional coordination.Experience directly managing a team, including hiring/interviewingand conducting formal performance reviews.Deep hands-on experience with Databricks (Spark), Boomi/Rivery, and Delta Lake patterns for scalablelakehouseprocessing. Strong experience with ETL/ELT design and implementation, including orchestration/ingestion into transformation workflows across curated layers. Experience partnering with application and database teams to define efficient, non-disruptive data access patterns for upstream SQL Server systems (read rep