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Engineering Manager (Data Engineering)

Quince· Data Engineering
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Bengalurufull-timePosted 4w ago

About the role

Engineering Manager to lead the Data Products team responsible for ETL pipelines and curated datasets across Quince’s core domains. Own datasets end-to-end, set modeling and quality standards, and partner with Analytics, Marketing, and Product.

Responsibilities

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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

  • 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.

Skills

Required:SQLPythonSparkData ModelingCloud Data WarehouseAirflow
Nice to have:dbt (preferred)Data Quality & Observability
Last updated Sep 23, 2026. Interested? Apply on the company site.
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