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Manager, Data Architecture

Ford
Dearborn, USA
Full-time
Mid · 3+ years experience
Hybrid
USD 132800-250800 yearly / year
Discovered 1 weeks ago
SQLPythonJavaScalaApache SparkKafka
Free

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Responsibilities

  • Define and evolve cloud data architecture patterns, standards, reference architectures, and roadmaps in partnership with Data Product Management, Enterprise Architecture, platform engineering, governance, security, and domain teams.
  • Partner with business and technology stakeholders to translate strategic business needs into scalable data architecture, data-product, and integration designs.
  • Guide teams in designing and implementing trusted, reusable, discoverable, and well-governed data products.
  • Develop and govern conceptual and logical data models for enterprise and domain data products.
  • Establish standards for data modeling, naming, schema design, data contracts, lineage, metadata, master/reference data, data quality, and lifecycle management.
  • Drive adoption of metadata-management and semantic capabilities, including the use of ontology-based business rules and semantic models where appropriate to support analytics and AI/agentic use cases.
  • Partner with domain teams to understand their data landscape, assess architecture readiness, identify capability gaps, and define practical improvement plans.
  • Serve as an architecture advisor and trusted point of contact for teams navigating the enterprise data ecosystem, helping connect them to the appropriate platform, product, governance, and enablement partners.
  • Define and promote reusable architecture patterns that reduce duplicate data movement, improve interoperability, and enable self-service consumption.
  • Ensure architecture designs meet requirements for security, privacy, compliance, resiliency, observability, data quality, and cost optimization.
  • Review solution designs and provide architecture guidance throughout the delivery lifecycle, balancing enterprise standards with business speed and practical implementation needs.
  • Collaborate with engineering teams to improve the usability and adoption of platform capabilities by identifying recurring customer friction, handoff gaps, and onboarding challenges.
  • Evaluate emerging cloud, data, metadata, semantic, and AI technologies and recommend where they can create value for Ford.
  • Communicate architecture decisions, tradeoffs, standards, and roadmaps clearly to both technical and non-technical audiences.

Minimum Qualifications

  • Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Analytics, or a related field, or equivalent professional experience.
  • 3+ years of experience in data architecture, data engineering, solution architecture, or a related technical role.
  • Experience designing and delivering modern cloud-based data platforms, pipelines, or data products in GCP, AWS, Azure, or a comparable cloud environment.
  • Strong experience with data modeling, including conceptual, logical, and physical models for operational, analytical, and event-driven use cases.
  • Experience with structured, semi-structured, and unstructured data; relational and NoSQL data stores; and data lake, lakehouse, warehouse, and data-mesh/data-product concepts.
  • Strong SQL skills and experience using Python, Java, Scala, or similar languages for data processing, automation, and integration.
  • Experience with distributed data-processing and streaming technologies, such as Apache Spark, Kafka, Pub/Sub, Dataflow, Flink, or comparable services.
  • Experience with data governance, metadata management, data lineage, data quality, security, and privacy practices.
  • Ability to influence across organizational boundaries and work effectively with product managers, engineers, data stewards, domain leaders, and executive stakeholders.
  • Strong written, verbal, and visual communication skills, including the ability to communicate complex architecture concepts in clear business terms.

Preferred Qualifications

  • Experience with Google Cloud Platform data services, including BigQuery, Cloud Storage, Pub/Sub, Dataflow, Dataproc, Composer, Dataplex, Data Catalog, and related services.
  • Experience designing event-driven and real-time data architectures.
  • Experience defining data contracts, reusable data-product standards, and domain-oriented data architecture practices.
  • Experience with semantic technologies, ontologies, knowledge graphs, or enterprise semantic models that enable trusted business rules and AI/agent use cases.
  • Experience with data cataloging, metadata platforms, and data observability tools.
  • Experience with DevSecOps, infrastructure as code, CI/CD, automated testing, and architecture-as-code practices.
  • Experience supporting AI, machine learning, generative AI, or agentic AI solutions through well-governed data and semantic foundations.
  • Experience in a large, complex enterprise environment with diverse business domains, legacy systems, and modern cloud platforms.
  • Automotive, manufacturing, mobility, financial, supply-chain, or connected-product data experience.

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