Senior 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 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. Pay Disclosure The salary range for this role is $111,000 - $138,700 for Atlanta based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs. The successful candidate may be eligible for annual performance bonus and equity awards. We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com. Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law. Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at candidateaccommodations@rivian.com. Candidate Data Privacy Rivian may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. Rivian may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. Rivian may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions. Please note that we are currently not accepting applications from third party application services. 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: 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 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.