Data & Analytics Engineer

Revelyst Outdoor•Irvine, CA

2d•$100,000 - $120,000

About The Position

This is a hands-on technical role supporting Revelyst’s data and analytics capabilities from its Irvine, California office. The position develops and supports data pipelines, data models, reporting solutions, and the analytics environment needed for reliable business decision-making. The role serves as a key technical resource for the enterprise data ecosystem, working across platforms such as ERP, CRM, e-commerce/B2B, custom applications, and other operational data sources. It partners with Finance, business analysts, application teams, and external technology partners to ensure data is accessible, reliable, appropriately governed, and structured for reporting and analytics. The successful candidate brings strong data engineering fundamentals, including hands-on Databricks and Spark experience, along with working business intelligence experience and an interest in growing into broader data architecture and platform ownership responsibilities.

Requirements

At least 3 years of experience in data engineering, business intelligence, analytics engineering, or a related technical role.
Strong SQL skills and experience working with relational and analytical datasets.
Experience with Git-based version control and engineering practices such as code review, CI/CD, and automated testing of data pipelines.
Hands-on experience developing and supporting ETL/ELT pipelines, data transformations, and data models.
Proficiency in Python for data engineering, transformation, and automation.
Hands-on Power BI experience, including data modeling, dashboards, reports, and semantic models.
Experience working with a cloud data platform, data warehouse, lakehouse, or similar analytics environment.
Experience integrating and working with data from multiple business systems, databases, APIs, or other data sources.
Strong troubleshooting and problem-solving skills, including the ability to investigate data-quality, pipeline, reporting, and performance issues.
Strong communication and collaboration skills, with the ability to work effectively with technical and business teams.
Equivalent professional experience and demonstrated technical capability may be considered in place of formal education requirements.

Nice To Haves

Experience with Microsoft Fabric, Azure Data Factory, Azure Synapse, Databricks, or similar modern cloud data platforms.
Experience integrating data from SAP or Oracle ERP, Salesforce CRM, e-commerce/B2B, and other enterprise business applications.
Experience working with financial, sales, inventory, customer, supply chain, or other operational business data.
Familiarity with Python or other scripting languages commonly used in data engineering and automation.
Experience supporting production data and reporting environments, including monitoring, troubleshooting, and performance optimization.
Exposure to data governance, data-quality management, AI/advanced analytics, or modern data architecture practices.
Experience working with an e-commerce/subscriptions/software business model.

Responsibilities

Design, develop, maintain, and support data pipelines and integrations across enterprise applications, APIs, databases, and cloud data platforms such as Databricks and Microsoft Fabric.
Build and maintain scalable data models, transformations, and ETL/ELT processes that provide reliable, usable data for reporting and analytics.
Implement pipeline orchestration, monitoring, alerting, and automated data-quality checks to keep production data flows reliable and observable.
Develop and support Power BI datasets, semantic models, dashboards, and reports for Finance, Sales, Operations, Supply Chain, and other business functions.
Monitor and support the Golf data and analytics environment, troubleshooting pipeline failures, reporting issues, performance problems, and data discrepancies.
Identify data-quality and normalization issues and partner with business and application owners to improve data accuracy, consistency, and governance.
Support and evolve the data architecture, including transitioning knowledge and ownership of the existing Microsoft Fabric environment from external partners.
Partner with business analysts, application teams, and technology partners to translate business and reporting needs into scalable technical solutions.
Identify opportunities to simplify and automate the data environment while helping build a foundation for future analytics, AI, and automation capabilities.