Staff Data Engineer

Location: Tempe, Arizona

Department: R&D

Location Type: HYBRID

Employment Type: FULL_TIME

Responsibilities

• 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.

• 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

• 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.

• You set the engineering quality bar. Reliable, efficient, documented, testable, maintainable — and you hold the team to the same standard.

• 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.

• 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