Staff Analytics Engineer

Rivian•Riverdale, GA

About The Position

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.

Requirements

Bachelor’s Degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, or a similar discipline).
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.
Deep proficiency in writing complex, optimized SQL queries, data manipulation, performance tuning, and understanding various SQL dialects.
Strong ability to write clean, efficient, and scalable Python code for data extraction, transformation, loading, and automation of data workflows.
Solid understanding of data warehousing concepts, dimensional modeling, and schema design (e.g., star schema, snowflake schema).
Proficiency with industry best practices and tools for collaborative software development, including version control (Git/GitHub/GitLab), testing, and CI/CD pipelines.
Strong analytical and problem-solving skills with a passion for designing and building efficient, maintainable, and scalable data systems.
Excellent communication and collaboration skills are essential, as you'll partner with and support colleagues across the business with varying levels of technical expertise.

Nice To Haves

Hands-on experience with dbt (data build tool) for data transformation and modeling.
Experience with cloud-based data warehousing solutions (e.g., Snowflake, Google BigQuery, Amazon Redshift) and related cloud services.
Experience with data quality, data security, and monitoring initiatives.
Experience with modern data ingestion tools like Fivetran, Airbyte, or similar.
Familiarity with at least one major BI tool (Tableau, Qlik, Power BI, Hex) with the ability to understand how data models support visualization needs.

Responsibilities

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