Senior Analytics Engineer

Position: Senior People Analytics Engineer
About Rivian

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract.

As a company, we constantly challenge what's possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.

Role Summary

Rivian is seeking a passionate and data-driven Senior Analytics Engineer to join our People Systems Team. In this pivotal role, you'll be instrumental in delivering impactful insights that drive strategic decision-making across the organization. You'll bridge the gap between complex business challenges and data-driven solutions through comprehensive stakeholder management, meticulous requirements gathering, and thorough data collection and research.

As a Senior Analytics Engineer, you'll leverage your expertise to build robust data pipelines, create intuitive and powerful dashboards, and ensure data quality and accessibility. You'll also play a key role in user education and training, empowering our teams to effectively utilize analytical tools and insights. This position requires a deep understanding of data warehousing principles, advanced SQL, and proficient Python for data transformation and automation.

The ideal candidate thrives in ambiguous environments, possesses exceptional problem-solving skills, and is adept at collaborating with diverse stakeholders to translate business needs into scalable data solutions. A significant focus of this role will be to spearhead the strategic migration towards a next-generation analytical toolset, identifying and implementing modern solutions that enhance efficiency, accessibility, and analytical capabilities.

Responsibilities

• Data Model Design & Development:
  Design, develop, and maintain robust and scalable data models within our data warehouse, ensuring data integrity and optimal performance for analytical consumption.
• ETL/ELT Pipeline Engineering:
  Build, optimize, and manage complex data pipelines (ETL/ELT) to ingest, transform, and integrate data from various disparate sources, ensuring accuracy, reliability, and timeliness.
• Data Quality & Governance:
  Implement and enforce data quality standards, monitor data pipelines, and troubleshoot data issues to ensure the reliability and accuracy of our analytical datasets.
• Performance Optimization:
  Identify and implement performance optimizations across data models and queries to enhance the speed and efficiency of data access for analysts and business users.
• Tooling & Infrastructure Development:
  Evaluate, recommend, and implement modern data tooling and infrastructure improvements to enhance our analytical capabilities and data platform.
• Cross-Functional Collaboration:
  
  Partner closely with data engineers, analysts, and business stakeholders to understand data requirements and translate them into well-engineered data solutions.
• Documentation & Best Practices:
  Create comprehensive documentation for data models, pipelines, and processes, and promote best practices for data engineering and analytics within the team.

Qualifications

• Education:
  
  Bachelor's Degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, or a similar discipline).
• Experience:
  
  Over 5+ years of proven experience in senior or staff positions focused on data engineering, analytics engineering, or similar roles with a strong emphasis on data infrastructure and modeling.
• Advanced SQL Expertise:
  Deep proficiency in writing complex, optimized SQL queries, data manipulation, performance tuning, and understanding various SQL dialects.
• Python for Data Engineering:
  Strong ability to write clean, efficient, and scalable Python code for data extraction, transformation, loading, and automation of data workflows.
• Data Warehousing Principles:
  Solid understanding of data warehousing concepts, dimensional modeling, and schema design (e.g., star schema, snowflake schema).
• Collaborative Software Development:
  Proficiency with…