Staff Data Engineer – Testable Functionality Rollout Plan

General Motors•Milford Charter Township, MI

1d•Hybrid

About The Position

The Testable Functionality Rollout Plan (TFRP) is GM’s framework for rolling out software and hardware functionality with confidence. TFRP connects functional intent, requirements, software, hardware, tests, and results into a single, traceable story so engineering teams can see exactly what changed, what was validated, and how stable a function is at any point in its lifecycle. Organizations such as Mechatronics use TFRP to plan and time their work, understand issues quickly, and help triage and drive fixes overnight. Our mission is to turn complex test and configuration data into clear, actionable insight that accelerates feature delivery and drives data-driven decisions across the vehicle ecosystem. We are looking for a TFRP Data Engineer – Software Developer to build the backbone of this visibility: a traceable gold-source data lake and visualization layer. In this role, you will ensure that all data describing software–hardware connections, configurations, tests, and outcomes is centralized, modeled, and exposed through intuitive dashboards and applications. Your work will create the single source of truth that powers end-to-end traceability, enables engineers to quickly understand impact and coverage, and gives leaders a real-time view of functional readiness and stability across programs.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Engineering, or a related field.
7+ years of professional experience in software and/or data engineering.
Strong hands‑on experience with Databricks or similar Spark‑based platforms, including: Building production ETL/ELT pipelines in PySpark.
Working with Delta Lake (or equivalent) for large‑scale data storage and querying.
Proficiency in Python for both data processing and backend service development.
Proficiency in React web stack and typescript.
Strong SQL and data modeling skills; experience designing schemas and tuning queries for performance at scale.
Experience building and operating backend services/APIs (e.g., Flask, FastAPI, Django, or similar).
Proven track record of owning critical batch or nightly pipelines, including monitoring, alerting, and incident management.
Strong communication skills and ability to work directly with cross‑functional partners (engineers, analysts, product leads).

Nice To Haves

Master’s degree in Computer Science, Software Engineering, Data Engineering, Engineering, or a related field.
10+ years of professional experience in software and/or data engineering.
Strong skills connecting end user UI with application integration and data source schema alignment.
SQL Power user with additional experience in graph database querying (cypher/ graphQL) and neo4j.
Deep experience with Databricks deployment tooling (e.g., dbx), CI/CD pipelines, and multi‑environment management.
Prior work with automated test infrastructures, CI/CD systems, or large‑scale log/telemetry processing.
Strong experience with Power BI (or similar BI tools), including data modeling, DAX, and performance optimization.
Familiarity with Microsoft Azure (e.g., Data Lake, Key Vault, Data Factory, Event Hubs/Service Bus).
Background in automotive, embedded systems, diagnostics, or other safety‑critical domains where test quality and rapid root‑cause analysis are essential.

Responsibilities

Develop and maintain backend APIs/services in Python (e.g., Flask/FastAPI) that provide structured access to processed test data and failure summaries.
Code and provide requirements for scalable UI & reporting initiatives utilizing React web application and both relational and graph database capabilities.
Integrate with upstream systems (test infrastructure, CI pipelines, file/object storage) and downstream consumers (internal web apps, reporting tools).
Implement robust error handling, logging, metrics, and tracing so incidents are visible and diagnosable without guesswork.
Partner with data analysts and engineering stakeholders to shape the semantic layer (tables, views, measures) that powers Power BI and other reporting tools.
Ensure nightly refreshes are predictable, well‑monitored, and clearly communicated to stakeholders.
Help simplify complex test/log data into intuitive, stable data models that engineers can easily query and build on.
Serve as the technical point of contact for the automated test analytics platform and its nightly processing — including incident response and root‑cause analysis.
Drive engineering standards for code quality, testing, documentation, and reviews across data and backend components.
Mentor engineers and analysts on Databricks, data modeling, and backend best practices, enabling the broader team to contribute safely and effectively.
Provide requirements, documentation, training videos, training classes, and message board technical support and RCA.
Provide Support for users on technical support on dashboard related issues.
Model and foster GM’s Core Values and Winning behaviors