Manager, Data Engineering

Revecore

2d•Remote

About The Position

We are seeking an experienced Manager for the Data Engineering team. This role reports to the Senior Director of Data Engineering. This is a hands-on, player and coach role. The Manager is expected to remain deeply involved in the design, implementation, and review of data pipelines and data models while also leading and developing a team of data engineers. Success in this role requires balancing technical execution with people leadership and delivery accountability. The Manager will influence and implement the technical direction of the data platform by proposing improvements, evaluating tradeoffs, and operationalizing solutions aligned with architectural direction set by senior leadership.

Requirements

8+ years of experience in data engineering, with at least 3–4 years in a people management or engineering lead capacity.
Proven track record of building and scaling data pipelines in a production, cloud-native environment.
Deep expertise in ETL and ELT design patterns, data modeling, batch and streaming data processing, and distributed data systems.
Strong proficiency in SQL and Python; experience with orchestration tools such as Apache Airflow or Prefect.
Deep, hands-on expertise with Snowflake as a primary data warehousing platform; experience with transformation frameworks such as dbt.
Demonstrated ability to build and own end-to-end data models that encode business logic, with a track record of validating data correctness across ingestion, transformation, and migration workflows.
Demonstrated experience implementing or operationalizing a data governance program at meaningful scale.
Experience with compliance frameworks (SOC 2, GDPR, CCPA) and collaborating with Legal and Security teams.
Exceptional communication skills with the ability to influence across technical and non-technical audiences.
Demonstrated ability to balance hands-on technical execution with coaching, delegation, and delivery accountability.
A quiet, distraction-free environment to work from.
A secure internet connection with consistent speeds greater than 20 Mbps for downloads and greater than 10 Mbps for uploads is required.
The workspace area accommodates all workstation equipment and related materials and provides adequate surface area to be productive.

Nice To Haves

Experience with cloud infrastructure on AWS or Azure, including services such as S3, Lambda, Event Hubs, or similar managed data services.
Background in or exposure to ML Engineering or feature store pipelines.
Experience in a high-growth technology company or similarly fast-paced environment.
BS or MS in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

Responsibilities

Design, build, and maintain scalable, high-availability data pipelines that ingest, transform, and serve structured and unstructured data across the enterprise.
Own the end-to-end ETL and ELT implementation in Snowflake, with strong hands-on expertise including data loading patterns, performance tuning, warehouse sizing, and cost optimization.
Partner with Analytics, Product, and ML Engineering teams to deliver clean, well-documented data models that accelerate business insights.
Evaluate and implement modern data stack tooling (for example dbt, Airflow, Spark) aligned to evolving organizational needs and standards.
Define and enforce SLAs for data freshness, quality, and availability across all critical data products.
Develop and maintain robust, well-documented data models that accurately reflect business processes, entities, and rules — ensuring the data team and its stakeholders share a common understanding of how data is defined and used.
Deeply understand the business context behind the data: where it originates, what it represents, how it flows through systems, and why it is used — and instill this understanding across the engineering team.
Champion data correctness as a first-class engineering concern — proactively identifying discrepancies, tracing root causes, and driving resolution with rigor and precision.
Collaborate closely with Analytics and Business stakeholders to translate business logic into reliable, reusable data models that produce trustworthy outputs for reporting, analysis, and product use cases.
Establish modeling standards and conventions (naming, grain definitions, relationship integrity) that promote consistency and reduce ambiguity across the data platform.
Own the full lifecycle execution of data — from ingestion and transformation through delivery and consumption — ensuring quality, integrity, and traceability at every stage.
Lead data migration efforts including planning, mapping, execution, and post-migration validation, ensuring data arrives at its destination complete, accurate, and in the expected format.
Design and enforce data validation frameworks that verify correctness during transport, transformation, and migration — catching issues before they reach downstream consumers.
Conduct and oversee data analysis to investigate pipeline behavior, validate business logic, and confirm that data outputs align with expected outcomes.
Document data flows, lineage, and transformation logic to ensure full auditability and support both operational and compliance needs.
Contribute to the architectural evolution of the data platform by proposing improvements, evaluating tradeoffs, and implementing solutions aligned with architectural direction and priorities set by senior leadership.
Establish engineering best practices including code review standards, release and deployment hygiene, incident response expectations, and on-call processes that scale with the team.
Promote a data quality culture by implementing automated testing, validation frameworks, and data observability tooling.
Lead and manage the day-to-day operations of a team of data engineers, ensuring clear task ownership, on-time delivery, and accountability across all active projects.
Partner with the Senior Director on workforce planning and hiring; lead interviews, candidate evaluation, and onboarding for new team members.
Conduct regular 1:1s, performance reviews, and career development conversations to support team members’ professional growth.
Build a psychologically safe, collaborative team environment that encourages innovation and feedback at all levels.
Mentor and coach engineers through hands-on technical leadership, pairing, and pragmatic guidance on design, implementation, and debugging.
Partner with the Senior Director, Legal, Security, and Compliance to implement and operationalize a data governance framework covering data lineage, cataloging, access controls, and data classification.
Implement and enforce data retention policies, PII handling standards, and audit logging practices across all data systems in alignment with company policies and regulatory requirements.
Support security audits, vendor assessments, and compliance reviews as a primary data engineering contributor, providing evidence, documentation, and technical context as needed.
Partner with Legal, Security, and Compliance teams to ensure data handling practices align with SOC 2, GDPR, CCPA, and other applicable regulatory requirements.
Translate the data engineering roadmap set by senior leadership into concrete project plans, milestones, and team assignments.
Actively manage project timelines, surface blockers early, and coordinate cross-functional dependencies to keep deliverables on track.
Serve as the primary day-to-day point of contact for cross-functional teams (Analytics, Product, ML Engineering), coordinating requests and aligning priorities with the engineering team’s capacity.
Escalate tradeoffs, risks, and prioritization conflicts with clear recommendations and options.