Senior Manager, Data Engineering

Job summary

- Remote - US: Select locations - Remote - Engineering - Full Time Dro*** is a Virtual First company. For this role, we are hiring in Zones 2 and 3. Please refer to our Compensation section below to see what neighborhoods fall under each Zone.

Benefits

Qualifications

• 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments.
• 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design.
• Reliability & Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines.
• Systems & Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers.
• Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals.
• PREFERRED
• Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy.
• AI-Forward Engineering: Experience integrating AI coding

Responsibilities

• We are seeking a Senior Manager, Data Engineering to lead the team responsible for the reliability, quality, cost, and velocity of Dro***'s core data platform.
• Data Quality & Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics.
• Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy.
• Cost & Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability.
• Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding

Skills

Degrees

Industry