Data Engineer

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 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.

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.

#LI-NG2

#FordMexico

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.