Sr. Data Engineer

Revelyst Outdoor•Irvine, CA

36d•$123,000 - $140,000•Onsite

About The Position

Revelyst is a collective of makers who design and manufacture performance gear and precision technologies. Our category-defining brands leverage meticulous craftsmanship and cross-collaboration to pursue innovation and redefine what is humanly possible in the outdoors. We are adding an experienced Senior Data Engineer who will lead the design, development, and optimization of robust data pipelines and enterprise data solutions. The person in this role will mentor junior team members, define best practices, and collaborate with cross-functional teams to drive data strategy and architecture. This role requires deep technical expertise and the ability to architect scalable, secure, and high-performance data systems. This position reports to the Manager, IT & Business Analytics and is located Onsite 4 days per week in Irvine, CA.

Requirements

Advanced SQL expertise
Strong experience with Azure technologies including ADF, Azure Databricks, Azure Data Lake Storage, and Azure Synapse
Expertise in data modeling, database optimization, and distributed data processing
Proficiency in Python or similar scripting languages
Experience with Spark or other big data processing frameworks
Experience with retail and sales data

Nice To Haves

Azure Data Engineer Associate or Architect-level certifications
Experience mentoring junior engineers
Experience with CI/CD for data pipelines and DevOps tooling
Experience specifically in ERP data with a focus on SAP

Responsibilities

Design, build, and optimize large-scale ETL/ELT pipelines using Azure Data Factory (ADF) and other cloud-native tools.
Architect and implement complex data models, database schemas, and data lakehouse structures.
Lead data integration initiatives across enterprise systems, ensuring reliability, quality, and performance.
Establish data engineering standards, governance, and best practices.
Troubleshoot and enhance production data workflows.
Collaborate with analytics, product, and business teams to deliver scalable data solutions.
Demonstrate strong self-motivation and ownership by proactively identifying improvements, risks, and opportunities within data systems.
Apply engineering best practices to prevent technical debt, ensure long-term maintainability, and mitigate scaling or performance issues.
Work closely with internal and external teams to support integrations, resolve technical issues, and ensure smooth data exchange across platforms.