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Analyst General/Engineering

The ADAS Data Analytics team is seeking a high-impact analytics professional to bridge the gap between physical automotive testing and cloud-based big data. In this role, you will be a key driver of the feedback loop for Ford’s ADAS features—such as BlueCruise and Automatic Emergency Braking—by transforming raw vehicle data into actionable engineering narratives. We are looking for a developer-minded analyst with expert SQL skills and hands-on experience in the GCP/BigQuery ecosystem (specifically using Dataform or similar tools) to build robust pipelines and high-fidelity visualizations. If you are a data storyteller who wants to see your code translate directly into physical vehicle behavior, this is the role for you. Technical Core Skills • Advanced SQL Proficiency: Expert-level ability to write, debug, and optimize complex queries, including window functions, CTEs, and nested data structures. • Google BigQuery Expertise: Deep understanding of BigQuery architecture, including partitioning, clustering, and slot utilization to manage large-scale datasets efficiently. • Query Performance Optimization: Proven track record of auditing and refining slow-running queries to reduce computational costs and improve processing speed. • Big Data Architecture: Familiarity with modern data warehouse design patterns, schema modeling (Star/Snowflake), and ETL/ELT pipelines. • Modern Data Transformation (Dataform/dbt): Experience using Dataform or dbt to manage data transformations, version control, and documentation (Highly Preferred). • Python for Data Analysis: Ability to use Python (Pandas, NumPy) for data manipulation, automation, and extending analytical capabilities beyond SQL (Preferred). Modern Workflow & AI Integration • AI-Assisted Development: Proficiency in leveraging Large Language Models (LLMs) like ChatGPT, Claude, or GitHub Copilot to accelerate code generation, SQL debugging, and documentation. • Rapid Prototyping: Ability to quickly move from a business question to a functional data model or dashboard using a mix of traditional tools and AI productivity boosters. Business Intelligence & Visualization • BI Tool Mastery: High proficiency in visualization platforms (e.g., Looker, Tableau, Power BI) to build intuitive, self-service reporting environments. • Data Storytelling: The ability to translate complex technical findings into clear, narrative-driven insights that non-technical stakeholders can act upon. • Actionable Metrics Design: Experience defining and tracking "North Star" metrics and KPIs that directly correlate with business growth and operational efficiency. Soft Skills & Strategic Thinking • Results-Oriented Mindset: A focus on "so-what" analytics—ensuring every report or insight has a clear path to driving business results. • Stakeholder Management: Ability to partner with product, engineering, and leadership teams to gather requirements and deliver data solutions. • Analytical Rigor: A disciplined approach to data quality, testing, and validation to ensure "one version of the truth."

Skills Required:

Analytical skills, Troubleshooting (Problem Solving)

Skills Preferred:

Testing, Data/Analytics dashboards, Business Intelligence, SQL, Data Analysis, Data Governance, Big Query, Python

Experience Required:

1 Year of Data Analytics Experience

Experience Preferred:

Education Required:

Bachelor's Degree

Education Preferred:

Additional Safety Training/Licensing/Personal Protection Requirements:

Additional Information :

***POSITION IS HYBRID*** - Data Engineering & Pipeline Development: Design, develop, and maintain robust data transformation workflows using SQL and Dataform (or similar tools) within the Google Cloud Platform (GCP) / BigQuery ecosystem. - Data Storytelling: Translate complex ADAS performance metrics into clear, compelling narratives for stakeholders. You will be responsible for showing the "why" behind the numbers. - Visualization & Dashboarding: Build and maintain high-fidelity data visualizations (e.g., Looker, Tableau, or PowerBI) that provide real-time insights into system performance and customer usage. - Impact-Driven Analysis: Identify trends and anomalies in ADAS data to drive improvements in feature safety, comfort, and reliability. - Cross-Functional Collaboration: Partner with software engineers and feature owners to define data requirements for future ADAS features.