{"id":2139963,"url":"https://alion.io/job/ford-motor-company-data-engineer-25","title":"Data Engineer","company":{"id":4094,"name":"Ford Motor Company","domain":"ford.com","url":"https://alion.io/company/ford-motor-company","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":96,"ghost_share":0,"stale_share":0.156,"repost_share":0.031,"time_to_fill_p50_days":3,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Naucalpan, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":42000,"max_usd":109000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2491},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"dbt","optional":false},{"name":"Edge AI","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Keras","optional":false},{"name":"Machine Learning","optional":false},{"name":"MQTT","optional":false},{"name":"Node-RED","optional":false},{"name":"OPC UA","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Time Series","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-09-29T17:22:36Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-24T04:34:31Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"About Ford and ATP \nFord 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.\nATP 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.\nAs 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.\nSight.Sensedetects anomalies in parametric data: Sense.Machine(MiniTerms) for machine performance anomaly prediction and optimisation; Sense.Tooling(AI ToolSense) for tooling life optimisation; Sense.Energyfor optimisation of supplies consumption; Sense.Stampingfor stamping process anomaly prediction and surface splits; and Sense.MILOfor in-process test quality anomaly detection.\nSight.Visiondetects anomalies in image data: Vision.Scoutfor in-station visual error proofing across manual and automated processes; Vision.Surfacefor surface defect detection in components and vehicle bodies through the complete manufacturing process; and Vision.Stationfor manual process monitoring and anomaly detection covering productivity, quality and ergonomics.\nThe Sight Vertical product portfolio areset 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.\nHow the Sight Vertical Works\nThe Sight Vertical operates through value-focused, cross-functional product teams across two product families: Sight.Senseand 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.\nThis 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.\nThe Opportunity\nWe 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.\nSight data is genuinely hard: tens of thousands of monitored assets, billions of waveforms annually, high-volume image and video data from Vision.Scout, Vision.Surfaceand 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.\nYou will work at the intersection of operational technology, cloud data engineering and machine learning enablement, across both parametric data and image-based detection.\nThis 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.\nThe Position\nYou 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.\nWorking 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.\nThis is a senior individual contributor position with indirect leadership expectations.\nYou will work primarily on Google Cloud Platform, Ford's strategic cloud platform, and engage with CoreDSand Core AI as the strategic data and ML platform direction for the Sight Vertical.\nRequires availability to travel internationally on occasion to understand plant data sources at source.\n4 Days On Site In GTBC FORD México (Naucalpan, México)\nAble to travel periodically to Europe and US.\nBuild and Operate Data Pipelines\nDesign, 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.\n\nWork 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.\n\nBuild 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.\n\nHandle high-volume unstructured image and video data for Vision products, including object storage design, lifecycle and cost management, frame sampling and metadata indexing.\n\nOwn pipelines through production operation: monitoring, alerting, cost, backfill, recovery and incident response.\n\nData Modelling, Quality and Semantics\nDesign 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.\n\nImplement data quality controls: validation, completeness, drift, latency and lineage - and make data health visible rather than assumed.\n\nModel plant, line, station, asset and tool hierarchies consistently across both product families, integrating with systems of record such as Maximo.\n\nOptimise storage, partitioning and query performance and cost in BigQuery.\n\nDefine and design data set lifecycle, historical, archiving, and purging approaches.\n\nEnabling Analytics and Machine Learning\nPrepare and serve training and inference datasets for parametric and image-based anomaly detection models, including labelling and annotation workflows, dataset versioning and feature availability.\n\nPartner with data scientists on the MLOps path to production - reproducible datasets, versioning, monitoring and retraining triggers.\n\nSupport edge inference requirements, ensuring data available on the machine matches what models were trained on.\n\nBuild the datasets behind value evidence and dashboards used by plants, Product Managers and the ATP ledger.\n\nGovernance, Security and Engineering Practice\nApply Ford data governance, privacy, retention and cybersecurity requirements across OT and IT boundaries, working with the Controls Engineer where plant-floor constraints apply.\n\nGive 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.\n\nManage access and identity appropriately for plant, product and enterprise consumers of Sight data.\n\nDefine and own reusable ingestion and onboarding patterns so adding a plant is configuration rather than a project.\n\nSet data engineering standards for the vertical - pipeline design, testing, documentation, naming, lineage and runbook expectations - and hold work to them through review.\n\nMentor and coach engineers working with Sight data, pairing on complex pipeline and modelling problems and explaining trade-offs rather than simply supplying answers.\n\nActively reduce single-person dependency on critical datasets and pipelines, ensuring at least one other engineer can maintain each of them.\n\nLead technical design for significant data platform changes, producing options, trade-offs and architecture decision records with the Software Architect.\n\nFunctional & Technical Knowledge\nMinimum Requirements\nBachelor's degree in Computer Science, Data Engineering, Software Engineering or a related technical field, or equivalent demonstrable experience.\n\nTypically 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.\n\nAdvanced SQL and strong Python, with the judgement to choose appropriate tools and patterns rather than defaulting to the familiar.\n\nPractical experience with a major cloud data platform - GCP required (BigQuery, Pub/Sub, Dataflow, Cloud SQL, Cloud Storage, Composer).\n\nExperience with batch and streaming data processing and with orchestration tooling.\n\nExperience of data modelling for analytical and operational use, and of data quality and validation practice.\n\nSoftware engineering fundamentals: version control, automated testing, CI/CD (GitHub Actions), infrastructure as code.\n\nAbility to work with imperfect, real-world industrial data and to trace problems back to source.\n\nDemonstrable experience mentoring and growing other engineers - pairing, design and code review, technical coaching and knowledge transfer.\n\nAbility to lead complex technical work end to end under ambiguity, and to communicate data trade-offs clearly to product, plant and non-technical stakeholders.\n\nFluent professional English.\n\nTeam player, problem solver, agile way of working.\n\nPreferred Requirements\nExperience with time-series, high-frequency sensor or waveform data at scale.\n\nExperience with unstructured image or video data at scale, including annotation pipelines and dataset versioning for computer vision models.\n\nExperience with industrial and OT data sources - PLC tag data, OPC-UA, MQTT, Modbus, historians, SCADA, energy metering.\n\nExperience supporting ML workloads and MLOps (Vertex AI, Keras/TensorFlow, feature stores, model monitoring).\n\nExperience with dbt, Dataform, Terraform or equivalent transformation and infrastructure-as-code tooling.\n\nFamiliarity with data governance and cataloguing in a large enterprise, and with OT/IT security segregation...","description_format":"text","description_chars":13970,"description_truncated":true,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"Spanish","level":"All levels","optional":true},{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Mexico","iso":"MX","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Energy & Utilities","Financial Services","Information Security"],"lifecycle":[{"event":"open","at":"2026-10-09T02:51:35Z"}],"visa":[],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.228,"p_room":0.35,"age_days":23,"expected_fill_days":3,"reasons":["conf:3","velocity","win:tail","comp:brand"],"computed_at":"2026-10-23T05:54:15Z"},"pay":null,"html_url":"https://alion.io/job/ford-motor-company-data-engineer-25","json_url":"https://alion.io/job/ford-motor-company-data-engineer-25.json","meta":{"generated_at":"2026-10-24T04:52:52Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":7068,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4094},"rest":"https://alion.io/mcp/rest/get_company?id=4094"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fford-motor-company-data-engineer-25"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fford-motor-company-data-engineer-25"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fford-motor-company-data-engineer-25"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/ford-motor-company-data-engineer-25\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fford-motor-company-data-engineer-25"}]}