The Role
Own the 2-3 year architecture and reliability of high-volume catalog, pricing, and inventory ingestion. Lead migrations from legacy batch to modern systems, define data SLAs, build observability and orchestration, mentor senior engineers, and resolve cross-team, high-severity data problems.
Summary Generated by Built In
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.
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.
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, not dashboards bolted on afterward.
Built pipeline orchestration infrastructure, not just DAGs. You have strong opinions about operability because you've inherited systems that weren't.
- You set the engineering quality bar. Reliable, efficient, documented, testable, maintainable — and you hold the team to the same standard.
Led a migration from legacy batch infrastructure (custom schedulers, daemon-based systems, cron pipelines) to modern architecture without taking down production. We'll go deep on this in the interview.
- Deep AWS in production: EKS, EC2 fleet management, SQS, RDS. Operated at scale, not just deployed into it.
- Kubernetes in production — workload behavior, compute right-sizing for variable job profiles, failure modes under load.
- Streaming in production: Kafka, Flink, Kinesis, or Redpanda. You've made the batch-vs-streaming call in both directions and can defend either.
- Cloud data warehouse architecture — Snowflake, BigQuery, or Databricks. Clustering, partitioning, cost management, mixed analytical and operational workloads.
- You use AI coding tools daily and have shipped production work because of it.
- You write architecture docs engineers trust and can brief a VP on the same decision. Both matter at this level.
- BS or MS in Computer Science, Engineering, or equivalent.
AI Fluency & Modern Tooling
At RevolutionParts, we expect team members to actively use modern tools — including AI-powered systems — to improve decision-making, productivity, and quality of work.
This includes:
- Using AI tools responsibly to accelerate research, analysis, documentation, and problem-solving
- Exercising strong judgment around data privacy, accuracy, and ethical use
- Continuously learning and adapting as AI capabilities evolve
Proven examples of using AI to improve outcomes in prior roles is expected.
RevolutionParts is proud to provide all full-time Revolutionaries with a comprehensive employment package including competitive compensation, career development, benefits, 401K match, parental leave, and many more valuable perks. You can learn more about our core-value driven culture at our career page.
RevolutionParts is an Equal Opportunity Employer; we value diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, gender orientation, gender identity or expression, sexual identity, sexual orientation, age, marital status, family status, genetic information, veteran status, or disability status.
Please Note: You will only receive correspondence through the GEM ATS or from a @revolutionparts.com email address. If you are receiving communication through any other platform or domain, it may be fraudulent, and we urge you to ignore the communication.
CompensationThe base pay range for this role is $170,000 – $185,000 per year.
Skills Required
- 10+ years in data or software engineering, with at least 3 years at Staff level owning architecture for high-volume production systems
- Production experience with Python and Spark/PySpark at petabyte scale (not notebooks); tuning Spark from first principles
- Designed and operated distributed job execution systems: dynamic compute provisioning, job isolation, variable workloads, resource contention handling
- Deep production experience with message queue architectures (fan-out, poison pill handling, dead letter queues, consumer lag at scale)
- Built observability from the ground up: monitoring, alerting, lineage, and pipeline health with clear SLAs and owners
- Built pipeline orchestration infrastructure (not just DAGs) with strong operability practices
- Led migration from legacy batch/cron/daemon systems to modern architectures without production downtime
- Deep AWS production experience including EKS, EC2 fleet management, SQS, and RDS
- Kubernetes in production experience: workload behavior, compute right-sizing, failure modes under load
- Streaming in production experience (Kafka, Flink, Kinesis, or Redpanda) and making batch-vs-streaming tradeoffs
- Cloud data warehouse architecture experience (Snowflake, BigQuery, or Databricks) including clustering, partitioning, and cost management
- Daily use of AI coding tools with shipped production work and proven examples of improving outcomes using AI
- Ability to write trusted architecture documents and brief executive leadership (VP-level)
- BS or MS in Computer Science, Engineering, or equivalent experience
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The Company
What We Do
RevolutionParts accelerates OEM parts and accessories sales for every major automotive brand. The company’s core focus is on delivering an enterprise-ready platform for parts retail and wholesale commerce channels. The RevolutionParts platform powers more than $425M in annual sales and reaches millions of unique buyers globally. For more information, visit www.revolutionparts.com.
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