Manager, Data Engineering
Source API remote eligibility restrictions: United StatesFull-TimeManager
Salary140,000 - 185,000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- Microsoft SQL ServerData engineeringSparkCI/CDDatabricks
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, or relevant experience.
- 10+ years of professional experience in software/data engineering, including building production data pipelines and platforms.
- 2+ years of experience in lead capacity including team leadership, task breakdown, and delivery ownership.
- Experience directly managing a team, including hiring, interviewing, and formal performance reviews.
- Deep hands-on experience with Databricks (Spark), Boomi/Rivery, and Delta Lake patterns.
- Strong experience with ETL/ELT design and implementation.
- Experience partnering with application and database teams for data access patterns in SQL Server environments.
- Demonstrated expertise implementing Medallion architecture with clear separation of concerns.
- Ability to select appropriate ingestion strategies based on source variability (APIs, CDC, file drops).
- Proven organizational skills and ability to adapt to changing business needs.
Responsibilities
- 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 by setting standards for maintainability, organization, and repeatability.
- Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, and delegating effectively.
- Drive implementation of the Medallion architecture (Bronze/Silver/Gold) leveraging Databricks, Boomi/Rivery, and Delta tables.
- Establish DataOps practices aligned with modern SDLC: CI/CD, branching/release patterns, code review standards, and operational readiness.
- Improve pipeline observability and operational reliability by implementing monitoring for freshness, failure modes, and quality.
- Partner with stakeholders to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable work.
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