Jobs and Careers

Staff Data Engineer

United StatesRemotefull_timeVerifiedPosted 15 Aug 2026

About the role

RevolutionParts is not just a pioneering force in the automotive eCommerce realm; we're actively seeking passionate and talented individuals to join our squad of Revolutionaries (yes, that's what we call ourselves!). As leaders in providing streamlined, user-friendly solutions, we empower automotive brands to maximize online sales. Our commitment to technology, top-notch customer service, and a profound understanding of the automotive market sets us apart. If you're ready to revolutionize the eCommerce space for automotive parts and accessories, consider joining our dynamic team of Revolutionaries.

The Role

Most data engineering roles hand you a Jira board. This one hands you a whiteboard and asks what should be on it.

RevolutionParts powers parts and accessories commerce for thousands of automotive dealers and OEMs across North America. The data behind all of it (catalog, pricing, inventory) moves through a high-volume ingestion system that has scaled with the business. It was the right architecture for where we were. It isn’t the right architecture for where we’re going.

We need someone who can keep this system reliable today while making it obsolete on a timeline they define.

The target architecture doesn't exist yet. The technical bar for this domain gets set by whoever takes this role. If that's an uncomfortable amount of open space, this probably isn't the right fit. If it sounds like the kind of problem worth leaving your current job for, read on.

Responsibilities

Strategic Leadership & Architectural Ownership

You are the technical authority for data ingestion at RevolutionParts. You lead through expertise, not authority.
Own the 2-3 year architectural vision for data ingestion. That means the destination, the migration sequence, the tradeoffs at each stage, and the criteria that determine when the current system has earned its retirement.
Set the engineering standards that govern how every team builds on and interfaces with core data infrastructure: schema design, data contracts, query optimization, observability. What you establish here becomes the organization’s baseline.
Shape technical strategy across Product, BI, Platform Engineering, and Executive Leadership. Not as an advisor. As the person who drives alignment, cuts through ambiguity, and owns the outcomes of complex multi-quarter initiatives from discovery through delivery.
Take ownership of the highest-severity, most ambiguous problems in the data domain: the ones that cross team boundaries, have no clear owner, and have already resisted resolution.

Execution & Operational Excellence

Hold ultimate accountability for the architecture and production performance of our catalog, pricing, and inventory ingestion systems, with the technical depth to make decisions no one else in the organization is positioned to make.
Define the reliability bar for data across the organization. Build the monitoring, alerting, and validation frameworks that turn data quality from a best-effort into a contractual commitment with clear SLAs and owners.
Make final, binding technical debt decisions for the ingestion domain, weighing immediate stability against long-term architectural health. Document the reasoning with enough clarity that it survives organizational change 18 months from now.
Elevate the technical ceiling of the data engineering organization through direct mentorship of Senior Engineers on distributed systems, high-volume database performance, and data modeling at scale. Your impact here compounds beyond your own output.

Requirements

10+ years in data or software engineering, at least 3 at Staff level or equivalent owning architectural decisions on high-volume production systems.

Python and Spark/PySpark at petabyte scale — production systems, not notebooks. You tune Spark from first principles: partition strategy, join optimization, dynamic allocation, skew diagnosis.

Designed and operated distributed job execution systems: dynamic compute provisioning, variable workload profiles, job isolation, and resource contention at scale.
Deep experience with message queue architectures in production: fan-out patterns, poison pill handling, dead letter queues, consumer lag at scale.
Built observability into systems that had none — monitoring, alerting, lineage, and pipeline health designed in from the ground up,