Staff Data Engineer

RevolutionParts•Tempe, AZ

About The Position

RevolutionParts is seeking passionate and talented individuals to join their team. As leaders in providing streamlined, user-friendly solutions, they empower automotive brands to maximize online sales. Their commitment to technology, top-notch customer service, and a profound understanding of the automotive market sets them apart. This role is for someone who wants to revolutionize the eCommerce space for automotive parts and accessories. The data engineering role involves taking ownership of the data ingestion system, ensuring its reliability today while defining and executing a plan to make it obsolete on a timeline they define. The target architecture does not exist yet, and the technical bar for this domain will be set by the person who takes this role. This position requires strategic leadership, architectural ownership, and operational excellence in managing high-volume data systems.

Requirements

10+ years in data or software engineering, with at least 3 years at Staff level or equivalent owning architectural decisions on high-volume production systems.
Expertise in Python and Spark/PySpark at petabyte scale, including tuning Spark from first principles (partition strategy, join optimization, dynamic allocation, skew diagnosis).
Experience designing and operating distributed job execution systems (dynamic compute provisioning, variable workload profiles, job isolation, 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).
Experience building observability into systems from the ground up (monitoring, alerting, lineage, pipeline health).
Experience building pipeline orchestration infrastructure, not just DAGs.
Proven ability to set and maintain high engineering quality standards (reliable, efficient, documented, testable, maintainable).
Experience leading a migration from legacy batch infrastructure to modern architecture without impacting production.
Deep AWS experience in production (EKS, EC2 fleet management, SQS, RDS) operated at scale.
Experience with Kubernetes in production (workload behavior, compute right-sizing, failure modes under load).
Experience with streaming technologies in production (Kafka, Flink, Kinesis, or Redpanda), including making batch-vs-streaming decisions.
Experience with cloud data warehouse architecture (Snowflake, BigQuery, or Databricks), including clustering, partitioning, cost management, and mixed workloads.
Daily use of AI coding tools and shipping production work with their assistance.
Ability to write trusted architecture docs and brief executives on decisions.
BS or MS in Computer Science, Engineering, or equivalent.

Nice To Haves

Proven examples of using AI to improve outcomes in prior roles.

Responsibilities

Serve as the technical authority for data ingestion, leading through expertise.
Own the 2-3 year architectural vision for data ingestion, including destination, migration sequence, tradeoffs, and retirement criteria for the current system.
Set engineering standards for data infrastructure, including schema design, data contracts, query optimization, and observability.
Shape technical strategy across Product, BI, Platform Engineering, and Executive Leadership, driving alignment and owning complex multi-quarter initiatives.
Take ownership of high-severity, ambiguous problems in the data domain that cross team boundaries.
Hold ultimate accountability for the architecture and production performance of catalog, pricing, and inventory ingestion systems.
Define the reliability bar for data across the organization, building monitoring, alerting, and validation frameworks with clear SLAs.
Make final, binding technical debt decisions for the ingestion domain, documenting reasoning for long-term clarity.
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.