Data & Analytics Engineer

Revelyst•Irvine, CA

1d•Onsite

About The Position

This hands-on technical role supports Revelyst’s data and analytics capabilities from its Irvine, California office. The engineer develops and supports data pipelines, data models, reporting solutions, and the analytics environment used for reliable business decision-making. The role works across enterprise platforms and operational data sources, including ERP, CRM, e-commerce/B2B, and custom applications, partnering with Finance, business analysts, application teams, and external technology partners to make data accessible, reliable, appropriately governed, and ready for reporting and analytics. The successful candidate brings strong data engineering fundamentals, including hands-on Databricks and Spark experience, working business intelligence experience, and an interest in developing broader data architecture and platform ownership capabilities.

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 with a cloud data platform, data warehouse, lakehouse, or similar analytics environment.
Experience integrating data from multiple business systems, databases, APIs, or other data sources.
Strong troubleshooting and problem-solving skills, including investigating data-quality, pipeline, reporting, and performance issues.
Strong communication and collaboration skills for working 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, or 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 for reliable reporting and analytics.
Implement pipeline orchestration, monitoring, alerting, and automated data-quality checks.
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 work with business and application owners to improve data accuracy, consistency, and governance.
Support and evolve 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 and help establish a foundation for future analytics, AI, and automation capabilities.