Staff Software Engineer

Job Description

About the Role

We are seeking an experienced Staff Software Engineer to lead the technical direction for the data collection and its ecosystem of integrations, automations, and end-to-end observability. Scaling data collection is key to enabling autonomous and eyes off driving. 

In this role, you will design and build the platform that ensures high-quality data flows from vehicles into our AI/ML and analytics stacks, with strong guarantees around data quality, traceability, and operational excellence. 

You will work closely with AI research engineering, data engineering, vehicle engineering, data science, data quality, product and operations teams to deliver robust, observable services and user experiences that scale across General Motors programs. 

What You’ll Do 

• Own the system end-to-end – architecture, implementation, and operations for web UI, APIs, and backend services that power data collection and vehicle configuration workflows. 

• Design and implement integrations between UX and core platforms (data warehouse/lake, streaming pipelines, vehicle telemetry, configuration management, API gateway, identity & access, taxonomy/metadata services). 

• Build automations and orchestration for config rollout, data ingest, validation, and feedback loops (e.g., scheduled jobs, event-driven workflows, rule-based triggers). 

• Establish end-to-end observability: metrics, logs, traces, and dashboards that cover data flows from vehicles endpoints to downstream consumers. 

• Raise the bar on data quality and governance by defining and enforcing contracts, validation checks, and schemas across various systems. 

• Define and evolve system architecture to meet GM’s standards for security, compliance, and reliability, including access control, auditability, and PII-safe data handling. 

• Create and standardize APIs and event schemas that make an easy-to-integrate platform for other applications and services. 

• Partner cross-functionally with: 

• AI research team to assess data integrity and mining quality to ensure we collected right amount of data via the data collection platform. 

• Data Engineering on ingestion, transformation, and storage patterns. 

• Vehicle Engineering on in-vehicle integration and configuration lifecycles. 

• Data Science & Analytics on data access patterns, features, and experimentation needs. 

• Data Quality & Taxonomy teams on metrics, definitions, and consistent metadata. 

• Define and track platform metrics (reliability, latency, throughput, data quality scores, correctness of deployments) and create a roadmap for scale, resilience, and faster development/deployment cycles. <