About Ford and ATP
Ford Motor Company has endured and thrived for more than 118 years by reimagining how things are built. Today, Ford is leading a once-in-a-generation transformation in manufacturing — scaling EVs, integrating humanoid robotics, additive manufacturing, AI vision, and digital platforms into one seamless operating system: the Ford Production System (FPS/FPS+) that operates within the FAST framework. Ford Motor Company is undergoing a historic industrial shift — from building great vehicles to building great systems that scale across them. The Advanced Industrial Technology & Platforms (AITP) organisation was created to operationalise that shift. Our job is not to deploy isolated tools; it is to build a system that learns from factory truth, transforms it into scalable platforms, and turns those platforms into enterprise business value. Advanced Industrial Technology and Platforms (ATP) is a new, centralised organisation designed to position Ford to lead industrial innovation. This team will accelerate our digital evolution and manufacturing excellence — advancing the modernisation of our Ford Manufacturing operations with cutting-edge technology. As part of Ford Manufacturing, ATP is designed to integrate directly into our manufacturing sites, partner across teams (GME, safety, and drive innovation from initial idea to full implementation.
ATP is led by Managing Director Yung Fung, reporting to Ford Chief Manufacturing Officer Bryce Currie. This structure includes four vertical business units — focused on specific technology stacks — and four horizontals — shared services designed to ensure scale, deployment, and operational consistency.
As part of the AITP organisation, the Sight Vertical is responsible for building an end-to-end business around AI/ML anomaly detection products, operating as two product families.
detects anomalies in parametric data: e (MiniTerms) for machine performance anomaly prediction and optimisation; g (AI ToolSense) for tooling life optimisation; for optimisation of supplies consumption; ng for stamping process anomaly prediction and surface splits; and for in-process test quality anomaly detection.
detects anomalies in image data: for in-station visual error proofing across manual and automated processes; e for surface defect detection in components and vehicle bodies through the complete manufacturing process; and n for manual process monitoring and anomaly detection covering productivity, quality and ergonomics.
The Sight Vertical product portfolio are set to converge on the Sight Anomaly Platform, which builds on Ford's Core Systems layer (Core OS, CoreDS, Core AI and CoreApps/FPS+). Together they form Ford's scalable platform to deliver, process quality assurance, and zero-downtime, zero-defect manufacturing.
How the Sight Vertical Works
The Sight Vertical operates through value-focused, cross-functional product teams across two product families: and , each led by a Head of Product Family. Product Managers lead the product problem, desired outcome and priority. Engineers and product designers work with them to discover and deliver effective solutions. Engineering Managers retain people, capacity, technical-system and engineering-health accountability.
This is a vertical-level role working across both product families. Priorities are agreed between the two Heads of Product and their Engineering Managers, so that your effort is directed by portfolio value. In addition, ATP Core provides the non-product engineering expertise the verticals depend on — enterprise standards,



architecture, industrial controls, cybersecurity and platform guidance. This role holds a matrix reporting line into ATP Core to ensure alignment with those standards and ready access to that expertise.
The Opportunity
We are looking for a Data Engineer to build and operate the data foundations of the Sight portfolio — the pipelines that carry factory truth from PLCs, sensors, cameras and plant systems into a platform where anomalies can be detected reliably, at scale, across Ford's global manufacturing footprint.
Sight data is genuinely hard: tens of thousands of monitored assets, billions of waveforms annually, high-volume image and video data from , e and n, event-driven and polled sources, mixed plant infrastructure, and legacy equipment that was never designed to be observed. Getting this right is what makes the models trustworthy — and model trust is what determines whether plants act on our products or ignore them.
You will work at the intersection of operational technology, cloud data engineering and machine learning enablement, across both parametric data and image-based detection.
This is a senior individual contributor role. As the senior data engineering voice in the Sight Vertical, you will set the data engineering standards others work to, mentor engineers working with Sight data, and be accountable for ensuring the data foundations do not depend on you personally to remain trustworthy.
The Position
You will report into the Sight Vertical Director, with a matrix reporting line into the ATP Core team. The Sight Vertical Director leads what you prioritise and holds performance and development accountability; ATP Core provides technical alignment, enterprise standards and access to non-product expertise.
Working embedded within product streams across both families, you will partner with the Software Architect, Controls Engineer, Senior Software Engineer, Software Engineers, data scientists, the Launch and Deployment Engineers and plant controls teams. You will provide technical direction and mentorship to engineers on data-related work.
This is a senior individual contributor position with indirect leadership expectations.
You will work primarily on Google Cloud Platform, Ford's strategic cloud platform, and engage with CoreDS and Core AI as the strategic data and ML platform direction for the Sight Vertical.
Requires availability to travel internationally on occasion to understand plant data sources at source.

Functional & Technical Knowledge
Minimum Requirements
Bachelor's degree in Computer Science, Data Engineering, Software Engineering or a related technical field, or equivalent demonstrable experience.

Typically 5+ years of hands-on data engineering experience, with a substantial track record of production pipelines and data platforms you have designed, built and operated at scale.

Advanced SQL and strong Python, with the judgement to choose appropriate tools and patterns rather than defaulting to the familiar.

Practical experience with a major cloud data platform — GCP required (BigQuery, Pub/Sub, Dataflow, Cloud SQL, Cloud Storage, Composer).





Experience with batch and streaming data processing and with orchestration tooling.

Experience of data modelling for analytical and operational use, and of data quality and validation practice.

Software engineering fundamentals: version control, automated testing, CI/CD (GitHub Actions), infrastructure as code.

Ability to work with imperfect, real-world industrial data and to trace problems back to source.

Demonstrable experience mentoring and growing other engineers — pairing, design and code review, technical coaching and knowledge transfer.

Ability to lead complex technical work end to end under ambiguity, and to communicate data trade-offs clearly to product, plant and non-technical stakeholders.

Fluent professional English .

Team player, problem solver, agile way of working.

Preferred Requirements
Experience with time-series, high-frequency sensor or waveform data at scale.

Experience with unstructured image or video data at scale, including annotation pipelines and dataset versioning for computer vision models.

Experience with industrial and OT data sources — PLC tag data, OPC-UA, MQTT, Modbus, historians, SCADA, energy metering.

Experience supporting ML workloads and MLOps (Vertex AI, Keras/TensorFlow, feature stores, model monitoring).

Experience with dbt, Dataform, Terraform or equivalent transformation and infrastructure-as-code tooling.

Familiarity with data governance and cataloguing in a large enterprise, and with OT/IT security segregation.

Experience of data privacy requirements where imagery or video of people is processed.

Experience acting as the senior or lead data engineer in a team, setting standards and owning a data platform's direction.

Exposure to manufacturing systems (Maximo, MES, GDPS 2.0) or automotive and other high-complexity manufacturing.

Additional European language (Spanish or German).

Success in Role
Sight data pipelines are reliable, monitored, documented and cost-effective, with data quality visibly managed.

Onboarding a new plant or asset group to the data platform becomes a configuration exercise, not a bespoke build.

Data scientists and engineers can access trustworthy, reproducible datasets without bespoke intervention.

A consistent common data model and asset hierarchy is in place across Sight products, supporting general replication.

Model performance is supported by demonstrable data lineage, labelling quality and drift monitoring.

Data governance, access control, privacy and cybersecurity requirements are met across OT and IT boundaries.

Data engineering standards and patterns you have defined are visibly used by other engineers across both product families.

Engineers you mentor demonstrably grow in autonomy and quality on data work; you are not a permanent bottleneck.

No critical dataset or pipeline depends on a single person.

Ford of Mexico is committed to being an equal opportunity employer. We believe that a diverse and inclusive workplace is essential to our success. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, pregnancy, veteran status, or disability status.
Our focus is to build the best team. If you think you can bring value to Ford, love to collaborate, prioritize and aim to deliver excellence in everything you do, we encourage you to apply!
We thank all candidates for their interest, but only those selected for an interview will be contacted.
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📌 Data Engineer (Naucalpan de Juárez)
🏢 Ford Motor
📍 Naucalpan de Juárez