Rivian is seeking a passionate and data-driven Staff 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 Staff 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.
- 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.
- Education: Bachelor’s Degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, or a similar discipline).
- Experience: 6+ years of proven experience in 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 industry best practices and tools for collaborative software development, including version control (Git/GitHub/GitLab), testing, and CI/CD pipelines.
- Problem-Solving & System Design: Strong analytical and problem-solving skills with a passion for designing and building efficient, maintainable, and scalable data systems.
- Communication & Collaboration: Excellent communication and collaboration skills are essential, as you'll partner with and support colleagues across the business with varying levels of technical expertise.
Preferred:
- DBT Experience: Hands-on experience with dbt (data build tool) for data transformation and modeling.
- Cloud Data Platforms: Experience with cloud-based data warehousing solutions (e.g., Snowflake, Google BigQuery, Amazon Redshift) and related cloud services.
- Data Quality & Governance: Experience with data quality, data security, and monitoring initiatives.
- Data Ingestion Tools: Experience with modern data ingestion tools like Fivetran, Airbyte, or similar.
- BI Tooling Experience: Familiarity with at least one major BI tool (Tableau, Qlik, Power BI, Hex) with the ability to understand how data models support visualization needs.