Engineering Manager (Data Engineering)

THE ROLE

Engineering Manager (Data Engineering)

We’re looking for an Engineering Manager to lead the Data Products team — the engineers who own the ETL pipelines and curated datasets behind Quince’s core business domains.

This is a delivery- and ownership-heavy leadership role. You’ll own the datasets that the business runs on: how they’re modeled, how reliably they land, how well they’re documented, and how much they cost to produce. You’ll lead a team of data engineers embedded against business domains, set the modeling and quality bar across them, and act as the primary engineering partner to Analytics, Marketing, and Product.

If you like turning messy upstream systems into trustworthy, well-modeled data products — and building the team and standards that keep them trustworthy — this role is for you.

Responsibilities

Team Leadership & Development

• Lead, grow, and develop a team of data engineers owning domain-aligned data products, including hiring, onboarding, coaching, performance management, and career growth.
• Build strong working relationships with engineers across the team, understand strengths and development areas, and establish clear growth plans.
• Ensure operational efficiency and actively participate in organizational initiatives with the objective of ensuring the highest customer value.

Data Products & Dataset Ownership

• Own the end-to-end lifecycle of domain datasets: ingestion, transformation, modeling, publication, documentation, and deprecation.
• Own delivery of the domain ETL roadmap, including prioritization, sequencing, commitments, and predictable execution across multiple business stakeholders.
• Own freshness, accuracy, and availability SLAs for critical datasets, along with the on-call and incident response process that protects them.
• Establish clear ownership and data contracts between the Data Products team, upstream service teams, and downstream consumers.

Data Modeling & Engineering Standards

• Set and enforce standards for data modeling, including dimensional and semantic layers, transformation patterns, testing, and code review across domains.
• Drive data quality and observability practices through checks, alerting, lineage, and root-cause discipline so issues are caught before consumers find them.
• Identify opportunities to improve reliability, freshness, modeling quality, delivery predictability, and cost.

Data Platform & Technical Operations

• Partner with the Data Platform team to translate domain needs into platform capabilities and drive adoption of self-serve tooling within the team.
• Manage the compute and storage cost of domain pipelines and drive measurable efficiency improvements.
• Understand upstream source systems and failure modes that impact downstream data.
• Drive improvements across reliability, data quality, freshness, SLA adherence, cost, and query efficiency.

Stakeholder & Cross-Functional Partnership

• Act as the trusted engineering counterpart for Analytics, Marketing, and Product leaders.
• Develop strong working relationships with business and analytics stakeholders and establish clear support and intake processes.
• Translate business requirements into scalable and reliable data products while balancing competing priorities.
• Manage stakeholder expectations, communicate trade-offs clearly, and maintain predictable delivery.

Organizational Impact

• Establish and maintain a clear ownership model for domains and datasets, with named owners and defined interfaces.
• Define and roll out modeling and transformation standards covering naming, layering, testing, documentation, and review expectations.
• Establish a healthy execution rhythm across intake and triage for ad-hoc requests, sprint planning, design reviews, on-call rotation, and stakeholder reporting.
• Drive adoption of self-serve ETL tooling within the team and feed real requirements back to the Data Platform team.
• Set a longer-term domain roadmap that aligns business priorities with technical investment and retires legacy pipelines.

Qualifications

Required

• 8+ years of experience in data engineering, including 3+ years directly managing engineers.
• Strong hands-on background with SQL, Python, and Spark, with production experience building and operating ETL/ELT pipelines at scale.
• Deep expertise in data modeling, including dimensional modeling, slowly changing dimensions, incremental and idempotent transformation patterns, and designing datasets for analytical consumption.
• Experience owning business-critical datasets end-to-end, with real accountability for freshness, correctness, and SLAs.
• Hands-on experience with a modern cloud warehouse such as Snowflake, BigQuery, Redshift, or similar.
• Experience with a workflow orchestrator such as Airflow or similar.
• Track record of delivering a multi-quarter roadmap across several competing business stakeholders on predictable timelines.
• Demonstrated ability to hire, retain, and grow strong engineers.
• Strong stakeholder management skills, including the ability to say no, explain why, and maintain strong relationships.
• Comfortable working in ambiguous, fast-moving environments where you help define the right way.

Preferred

• Experience using dbt for transformations and modeling, and building a reusable modeling framework across domains.
• Experience with data quality and observability tooling such as Monte Carlo, Great Expectations, or in-house equivalents.
• Familiarity with data mesh / domain-ownership operating models and data contracts.
• Experience with Spark for large-scale transformations and Kafka or CDC-based ingestion.
• Familiarity with modern data stack concepts, including lakehouse architectures, columnar storage, and open table formats.
• Experience with a BI/semantic layer such as Looker or similar and partnering closely with analytics teams.
• Experience with AWS and managing pipeline cost at scale.