The Role
Build and maintain scalable GCP-based ETL/ELT pipelines using Python, BigQuery, and Google Cloud Storage. Support machine learning infrastructure through feature stores, model data feeds, Vertex AI integration, and MLOps workflows. Ingest data from APIs, streaming platforms, and databases; optimize SQL and BigQuery architecture; implement CI/CD, testing, data quality checks, monitoring, and governance. Collaborate with data scientists, ML engineers, and product managers to operationalize production machine learning systems.
Summary Generated by Built In
We are looking for someone with advanced Python programming skills who applies robust software engineering principles to data problems. You will collaborate closely with Data Scientists, ML Engineers, and Product Managers to build the scalable, automated pipelines required to train, deploy, and monitor machine learning models in production.
Key Responsibilities
- GCP Pipeline Development: Design, build, and maintain highly scalable ETL/ELT data pipelines using Python and GCP-native data processing tools (e.g., Cloud Run, Cloud Functions).
- AI/ML Infrastructure Support: Engineer feature stores, robust data feeds specifically optimized for machine learning training and inference. Work closely with ML Engineers to operationalize models using Vertex AI.
- Data Integration & Ingestion: Write clean, modular Python code to ingest data from diverse sources (APIs, streaming platforms, on-prem databases) into BigQuery and Google Cloud Storage (GCS).
- System Optimization: Optimize BigQuery architecture, partition/cluster tables, and tune complex SQL queries to ensure performance and cost-efficiency at a massive scale.
- Software Engineering Best Practices: Champion best practices in Python development, including version control (Git), CI/CD pipelines (Cloud Build / GitHub Actions), code reviews, and comprehensive unit/integration testing.
- Data Quality & Governance: Implement robust data quality checks, alerting, and monitoring to ensure the data feeding our AI models is accurate and reliable.
Qualifications
Required Qualifications
- Degree: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience).
- Experience: 4 to 6 years of professional experience in Data Engineering, Software Engineering, or a closely related field.
- Advanced Python: Deep expertise in Python programming. You should be highly comfortable with:
- Data processing and ML-adjacent libraries (e.g., PySpark, Pandas, NumPy).
- API development
- Writing efficient and production-grade code.
- GCP Mastery: Proven, hands-on experience designing and operating data architectures on Google Cloud Platform. Must have strong experience with:
- BigQuery (advanced SQL, architecture, and optimization).
- Google Cloud Storage (GCS).
- Compute/Serverless (Cloud Functions, Cloud Run).
- AI/ML Acumen: Experience working alongside Data Science teams. A strong understanding of the ML lifecycle, feature engineering, and the data requirements for model training and deployment.
MLOps: Understanding of MLOps principles, model registry, and continuous training pipelines.
Preferred Qualifications
- Vertex AI: Direct experience interacting with or deploying pipelines using Google Cloud's Vertex AI platform.
- Streaming Technologies: Familiarity with real-time data processing using Google Cloud Pub/Sub and streaming Dataflow jobs.
- Infrastructure as Code: Experience managing GCP resources using Terraform.
- Containerization: Proficiency with Docker.
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience
- 4 to 6 years of professional experience in Data Engineering, Software Engineering, or a closely related field
- Deep expertise in Python programming, including PySpark, Pandas, NumPy, API development, and production-grade code
- Hands-on experience designing and operating data architectures on Google Cloud Platform
- Strong experience with BigQuery, including advanced SQL, architecture, and optimization
- Strong experience with Google Cloud Storage
- Strong experience with Cloud Functions and Cloud Run
- Experience working alongside data science teams and understanding machine learning lifecycle, feature engineering, and model data requirements
- Understanding of MLOps principles, model registry, and continuous training pipelines
- Direct experience with Vertex AI and deploying pipelines
- Familiarity with Google Cloud Pub/Sub and streaming Dataflow jobs
- Experience managing GCP resources using Terraform
- Proficiency with Docker
Ford Motor Company Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Ford Motor Company and has not been reviewed or approved by Ford Motor Company.
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Healthcare Strength — Medical, dental, and vision coverage start on day one with options that include zero-premium plans, free mental health support, and wellness resources. For represented hourly employees, health plans are described as low-cost with strong coverage value.
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Retirement Support — A 401(k) with company match and additional company contributions is available from day one, alongside life and disability coverage. Pension eligibility in certain situations and financial-planning support reinforce long‑term security.
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Parental & Family Support — Paid parental leave, fertility, surrogacy, and adoption benefits, plus a ramp‑up program for returning parents, reflect a family‑focused package. Flexible Family Care days and generous time‑off options help address short‑term caregiving and personal needs.
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The Company
What We Do
Ford is a global company with shared ideals and a deep sense of family. From our earliest days as a pioneer of modern transportation, we have sought to make the world a better place – one that benefits lives, communities and the planet. We are here to provide the means for every person to move and pursue their dreams, serving as a bridge between personal freedom and the future of mobility. In that pursuit, our 186,000 employees around the world help to set the pace of innovation every day.
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