Senior Platform Engineer, Data Platforms

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

We are looking for a Senior Platform Engineer to own the infrastructure, automation, security, and reliability of Rivian’s Data Platform, built on Databricks and AWS. This is a platform and DevOps role at its core: you will treat our data platform as a product, providing data engineers, analysts, and data scientists with a secure, well-governed, observable, and cost-efficient environment where they can move fast without breaking things. You will define and manage the platform as code with Terraform, harden its security posture, build the CI/CD and deployment patterns the rest of the team relies on, and establish the monitoring and observability needed to keep the platform healthy at scale.

Responsibilities

Databricks Platform Ownership: Administer, tune, and evolve our Databricks Lakehouse environment workspace configuration, compute policies, serverless and classic compute, Unity Catalog, Lakeflow, and Databricks Asset Bundles (DABs), ensuring it is performant, reliable, and seamlessly integrated with the wider ecosystem.

Infrastructure as Code: Design and manage the platform’s AWS and Databricks infrastructure with Terraform, ensuring every workspace, catalog, policy, and integration is reproducible, version-controlled, and deployed through automated pipelines rather than manual changes.

Platform Security & Governance: Own the data platform's security posture, including IAM roles and least-privilege access, SSO/SCIM identity management, secrets management, network isolation and private connectivity, encryption, and audit logging. Configure and manage enterprise data governance, lineage, and metadata through Unity Catalog so our data remains a trusted asset.

Observability & Monitoring: Build and maintain platform observability, monitoring, alerting, and dashboards for pipeline health, job SLAs, cluster utilization, query performance, and platform costs, using Databricks system tables and our monitoring stack, so issues are caught before users notice them.

CI/CD & Developer

Experience

Design GitLab CI/CD pipelines, DAB deployment patterns, and paved-road templates that let data engineers ship pipelines and dbt models to production safely and quickly. Champion testing, code review, and environment promotion standards.

Reliability & Cost Optimization: Drive capacity planning, incident response, and root-cause analysis for platform issues. Continuously optimize compute configurations and workloads for performance and cost (FinOps).

Architectural Influence: Act as a technical consultant within the team, proactively propose infrastructure improvements, evaluate new Databricks and AWS capabilities, and keep our stack at the cutting edge.

Collaborative Engineering: Partner with data engineers and data scientists building in Python, SQL, and dbt, supporting orchestration via Databricks Workflows and Managed Airflow, and ingestion via Lakeflow and Fivetran to translate their needs into robust platform capabilities.

Qualifications

5+ years of experience in platform, DevOps, cloud, or data infrastructure engineering, including significant hands-on experience operating production data platforms.

Deep, hands-on expertise with the Databricks ecosystem: workspace and account administration, Unity Catalog, compute/cluster policies, Databricks Asset Bundles, Lakeflow, Databricks Workflows, and system tables. Other DWH/DataLake/Data Lakehouse platforms experience will be considered a solid asset.

Strong experience with AWS infrastructure and services (IAM, S3, VPC/networking, ECS, Lambda) and securing cloud environments or other cloud providers' equivalents..

Proven proficiency with Terraform or CloudFormation for infrastructure as code, ideally including the Databricks Terraform provider, with a strong grasp of module design, state management, and automated plan/apply workflows.

Solid programming skills in Python and expert-level SQL for automation, tooling, and platform diagnostics.

Practical knowledge of platform security: least-privilege IAM, secrets management, SSO/SCIM, network isolation, encryption, and audit/compliance requirements.

Experience implementing observability and monitoring for data platform metrics, logging, alerting, SLAs, and cost monitoring.

Working knowledge of dbt and modern ELT patterns, and experience supporting orchestration with Managed Airflow or Databricks Workflows.

Experience building CI/CD pipelines (GitLab CI/CD preferred) for infrastructure and data workloads.

A platform mindset: a track record of proposing and implementing improvements that made systems more scalable, secure, and self-service.

Nice to have

• Fivetran or similar managed ingestion tools, data quality and monitoring frameworks, Databricks or AWS certifications, and FinOps practices for data platforms.