Data Engineer

Ford México; Naucalpan de Juárez; Presencial; MXN 900 000 - 1 500 000; Tempo integral

Resumo da oferta

Ford Mexico is seeking a senior Data Engineer to build and operate the Sight portfolio data foundations. You will design data pipelines from plant OT sources into GCP, create robust data models, and ensure data quality and governance across industrial environments.

You will mentor engineers and collaborate with data scientists on MLOps. Based in Naucalpan, Mexico, this senior IC role requires 5+ years of experience, fluent English, and strong SQL/Python skills, with travel as needed to optimize

Qualificações

• Bachelor’s degree in Computer Science, Data Engineering or related field or equivalent experience.
• Typically 5+ years of hands-on data engineering experience with production data platforms.
• Advanced SQL and Python with ability to select appropriate tools for data problems.
• Experience with major cloud data platforms, especially Google Cloud (BigQuery, Pub/Sub, Dataflow).
• Experience with batch and streaming processing and data modeling for analytical/operational use.

Responsabilidades

• Build, operate and scale data pipelines from OT sources into a cloud data platform.
• Design data models and semantic layers for cross-product, cross-plant use.
• Implement data quality, lineage, and governance across OT/IT boundaries.
• Collaborate with data scientists on MLOps, feature stores and dataset versioning.
• Mentor engineers and lead data platform initiatives with minimal single-point dependency.

Conhecimentos

Fluent English

5+ years data engineering

Advanced SQL

Python proficiency

Formação académica

Bachelor's degree in Computer Science or related field

Ferramentas

GCP (BigQuery, Pub/Sub, Dataflow, Cloud SQL)

Cloud storage and Cloud Composer

Data modeling and orchestration tooling

dbt or similar transformation tooling

Descrição da oferta de emprego

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, etc.) and drive innovation from initial idea to full implementation.

Job Description

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, etc.) and drive innovation from initial idea to full implementation.

How The Sight Vertical Works

The Sight Vertical operates through value-focused, cross-functional product teams across two product families: Sight.Sense and Sight.Vision, 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 Vision.Scout , Vision.Surface and Vision.Station , 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.

Responsibilities

4 Days On Site In GTBC FORD México (Naucalpan, México)

Able to travel periodically to Europe and US.

• Build and Operate Data Pipelines: Design, build, test and operate reliable ingestion and processing pipelines from plant OT sources — PLC event data, sensor streams, waveform captures, energy meters and image capture — into GCP.
• Work with both event-driven ('notifier') and polled ('questioner') acquisition models, adapting to plant-level infrastructure choices such as CrossPLC (Ford developed middleware), Node-RED, DeviceWise, HiveMQ.
• Build batch and streaming pipelines on GCP — Pub/Sub, Dataflow, Cloud Run/Functions, Cloud Composer, BigQuery and Cloud SQL/PostgreSQL — with infrastructure and pipelines defined as code.
• Handle high-volume unstructured image and video data for Vision products, including object storage design, lifecycle and cost management, frame sampling and metadata indexing.
• Own pipelines through production operation: monitoring, alerting, cost, backfill, recovery and incident response.

• Data Modelling, Quality and Semantics: Design and maintain data models and semantic layers that make anomaly detection data usable across products and plants — including the common data model that enables global interoperability and rapid replication.
• Implement data quality controls: validation, completeness, drift, latency and lineage — and make data health visible rather than assumed.
• Model plant, line, station, asset and tool hierarchies consistently across both product families, integrating with systems of record such as Maximo.
• Optimise storage, partitioning and query performance and cost in BigQuery.
• Define and design data set lifecycle, historical, archiving, and purging approaches.

• Enabling Analytics and Machine Learning: Prepare and serve training and inference datasets for parametric and image-based anomaly detection models, including labelling and annotation workflows, dataset versioning and feature availability.
• Partner with data scientists on the MLOps path to production — reproducible datasets, versioning, monitoring and retraining triggers.
• Support edge inference requirements, ensuring data available on the machine matches what models were trained on.
• Build the datasets behind value evidence and dashboards used by plants, Product Managers and the ATP ledger.

• Governance, Security and Engineering Practice: Apply Ford data governance, privacy, retention and cybersecurity requirements across OT and IT boundaries, working with the Controls Engineer where plant-floor constraints apply.
• Give particular attention to imagery of people, where Vision.Station monitors manual processes and ergonomics — applying privacy, consent, retention and works council requirements with HR, Legal and local plant leadership.
• Manage access and identity appropriately for plant, product and enterprise consumers of Sight data.
• Define and own reusable ingestion and onboarding patterns so adding a plant is configuration rather than a project.
• Set data engineering standards for the vertical — pipeline design, testing, documentation, naming, lineage and runbook expectations — and hold work to them through review.
• Mentor and coach engineers working with Sight data, pairing on complex pipeline and modelling problems and explaining trade‑offs rather than simply supplying answers.
• Actively reduce single‑person dependency on critical datasets and pipelines, ensuring at least one other engineer can maintain each of them.
• Lead technical design for significant data platform changes, producing options, trade‑offs and architecture decision records with the Software Architect.

Qualifications

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 group, 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 global 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.

About Us

At Ford Motor Company, we believe freedom of movement drives human progress. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career and help us define tomorrow’s transportation.

About The Team

We build world‑class vehicles in our plants around the world, constantly developing new technologies and processes to further increase our efficiency. It’s our people who really make the difference. We encourage all of our employees to think outside the box to help us find better and more efficient ways to manufacture our vehicles.