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Quince is a manufacturer-to-consumer (M2C) fashion and accessories brand on a mission to deliver high-quality essentials at fair prices sustainably. The company operates across apparel, accessories, and home goods, serving tens of millions of customers globally through a direct-to-consumer model that eliminates traditional retail layers.
You will lead the Data Products team, a group of data engineers who own the ETL pipelines and curated datasets powering Quince's core business domains. This is a delivery-heavy leadership role combining hands-on technical ownership with people management.
Key responsibilities include:
**Team Leadership & Development:** Hire, onboard, coach, and develop data engineers; build strong working relationships; establish clear growth plans; ensure operational efficiency.
**Data Products & Dataset Ownership:** Own the end-to-end lifecycle of domain datasets (ingestion, transformation, modeling, publication, documentation, deprecation); deliver the domain ETL roadmap with clear prioritization and predictable execution; own freshness, accuracy, and availability SLAs for critical datasets; establish data contracts between upstream service teams and downstream consumers.
**Data Modeling & Engineering Standards:** Set and enforce standards for data modeling (dimensional, semantic layers, transformation patterns, testing, code review); drive data quality and observability practices through checks, alerting, and lineage; identify opportunities to improve reliability, freshness, modeling quality, and cost.
**Data Platform & Technical Operations:** Partner with the Data Platform team to translate domain needs into platform capabilities; manage compute and storage costs; understand upstream source systems and failure modes; drive improvements across reliability, data quality, freshness, SLA adherence, 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 stakeholders; translate business requirements into scalable data products; manage expectations and maintain predictable delivery.
**Organizational Impact:** Establish and maintain a clear ownership model for domains and datasets; define and roll out modeling and transformation standards; establish a healthy execution rhythm (intake, triage, sprint planning, design reviews, on-call rotation); drive adoption of self-serve ETL tooling; set a longer-term domain roadmap aligned with business priorities.
**Requirements:**
- 8+ years of data engineering experience, including 3+ years directly managing engineers
- Strong hands-on background with SQL, Python, and Spark; 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 (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 and maintain relationships
- Comfortable working in ambiguous, fast-moving environments where you help define the right way
**Preferred:**
- Experience using dbt for transformations and modeling; building a reusable modeling framework across domains
- Experience with data quality and observability tooling (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 (lakehouse architectures, columnar storage, open table formats)
- Experience with a BI/semantic layer (Looker or similar) and partnering closely with analytics teams
- Experience with AWS and managing pipeline cost at scale
