We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves.

Are you ready to change the way the world moves?

As a Program and Launch Management Analytics (PLMA) Data Engineering Manager, you will be at the heart of our data ecosystem, leading the team that builds and maintains data pipelines that support PLMA Analytics. You and your team will be responsible for designing, developing, and maintaining the foundational data assets and services that empower Artificial Intelligence, Data Science and Software Engineering. You'll also play a pivotal role in the collaboration of Ford’s Data Hub strategy, contributing to domain focused warehouses that represent the single source of truth for the enterprise. You'll be a champion for data engineering standardization by providing design input on new data engineering capabilities and implementing those capabilities across the PLMA datasets.

This is a fantastic opportunity for an experienced data engineering manager to make a significant impact. You'll be responsible for guiding the team in designing effective data curation solutions, prioritizing tasks, making timely decisions, and ensuring the delivery of high-quality results. Your expertise in data governance, customer consent, and security standards will be crucial in ensuring we operate responsibly and ethically with data.

You'll have...

Bachelor’s degree in Computer Science, Information Technology, Information Systems, Data Analytics, or a related field.

8+ years of experience in complex data environments, demonstrating increased responsibilities and achievements.

Expertise in programming languages such as Python or Scala, and strong SQL skills.

Experience with ETL/ELT processes, data warehousing, and data modeling.

Experience with CI/CD pipelines, Docker, Git/Gerrit, and experience designing resilient deployment strategies and sophisticated release management.

Familiarity of data governance, privacy, quality, and monitoring.

Proven experience in implementing sophisticated testing strategies, driving quality tool adoption, establishing comprehensive code review processes, and setting observability standards with advanced monitoring and proactive alerting.

5+ years of experience within the automotive industry or related product development environments and product lifecycle management.

5+ years of experience in leading software or data engineering teams, with a focus on team development and project success.

5+ years of experience in Big Data environments or expertise with Big Data tools, including data processing frameworks and data modeling.

In-depth knowledge and practical experience with Google Cloud Platform services.

Proven experience in monitoring and optimizing costs and compute resources in hyperscaler platforms.

Significant experience leveraging Generative AI and LLMs to optimize data engineering workflows (e.g., automated code generation, documentation, or metadata management).

Even better, you may have…

Master's degree in Computer Science, Engineering, or a related field.

Expertise in GCP based data engineering services like BQ, Dataflow, Airflow, Dataform, Datastream, Apache Beam, Cloud Run, Cloud Functions

Experience in managing and scaling serverless applications and clusters, focusing on resource optimization and robust monitoring and logging strategies.

Proficiency in streaming technologies such as Kafka and Pub/Sub, along with experience in Open Shift.

Experience with AI architecture and AI enabling tech (graph database, vector database, etc)

Familiarity with data visualization tools (e.g., Power BI, Tableau).

Working knowledge of ontology, semantic modeling, and related technologies

You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

What you'll do...

Lead, mentor, and develop a high-performing team of local and remote Portfolio Data Engineers, fostering a culture of collaboration, innovation, and continuous improvement.

Strategically prioritize and manage team workloads, ensuring effective task allocation and resource capacity to support team goals.

Provide expert technical guidance and mentorship, ensuring adherence to best practices, coding standards, and architectural guidelines.

Act as the Chief Data Technical Anchor for the PLMA domain, resolving critical incidents through Root Cause Analysis (RCA) and implementing permanent, resilient architectural fixes.

Oversee the design, development, maintenance, scalability, reliability, and performance of data platform pipelines, aligning them with business needs and strategic objectives.

Contribute to the long-term strategic direction of the Data Platform by proactively identifying opportunities for best practice adoption and standardization.

Champion data quality, governance, and security standards, ensuring compliance and safeguarding sensitive data assets.

Enhance efficiency and reduce redundancy by consolidating common tasks across teams.

Effectively communicate decisions to stakeholders, building strong relationships and ensuring alignment on data initiatives.

Maintain awareness of industry trends and emerging technologies to inform technical decisions.

Lead the implementation of customer requests into data assets, ensuring optimized design and code development.

Guide the team in delivering scalable, robust data solutions and contribute hands-on to critical projects, including design and code reviews.

Lead technical decisions that drive data innovation and resilience.

Demonstrate full stack cloud data engineering expertise, covering automation, versioning, ingestion, integration, transformation, optimization, and data modeling.

Engage in agile planning, including scope, work breakdown structure, as well as roadblock resolution.

Design solutions for cost and consumption optimization, scalability, and performance.

Collaborate with Data Architecture and stakeholders on solution design, data consolidation, retention, purpose of use, compliance, and audit requirements.

Drive engineering excellence by establishing and monitoring SWE-centric quality metrics (including DORA metrics and P99 latency targets).