Ford Motor Company
Ford Motor Company
„Ford Motor Company“ yra Jungtinėse Amerikos Valstijose veikianti automobilių gamintoja, kurios istorija prasidėjo 1903 metais. Be transporto priemonių kūrimo ir gamybos, bendrovė plečia dėmesį į judumo paslaugas, skaitmeninę patirtį ir programinę įrangą. „Ford“ taip pat vykdo tvarumo iniciatyvas ir siekia aplinkosaugos tikslų, atspindėdama požiūrį, siejantį transportą, inovacijas, poveikį bendruomenei ir atsakingą verslo praktiką.

Full-Stack Data Engineer at Ford

Build scalable GCP data pipelines and platforms for Ford’s enterprise technology organization. Support governance, security, automation, and performance improvements.

Aprašymas

  • Create and maintain scalable ingestion and curation pipelines from varied data sources.
  • Standardize and improve data quality so it is ready for analytics.
  • Develop efficient data pipelines with Python, SQL, and DBT or Dataform.
  • Support end-to-end development and dependable data movement from source to insight.
  • Build and operate GCP data platforms using BigQuery, Dataflow, Pub/Sub, and Cloud Functions.
  • Apply data governance policies, access controls, and cloud security practices.
  • Manage data workflows and provision cloud infrastructure with Astronomer and Terraform.
  • Improve pipeline and storage performance, scalability, efficiency, resource use, and cost effectiveness.
  • Work with data and application architects, service owners, and cross-functional teams to establish practices, design patterns, and frameworks.
  • Automate platform operations to strengthen reliability, data quality, and efficiency.
  • Explain complex technical decisions to technical and non-technical stakeholders.
  • Find ways to improve the data platform and extend its capabilities.
  • Turn business needs into effective data asset designs and efficient code.
  • Document data engineering processes and encourage knowledge sharing.

Reikalavimai

  • Bachelor’s degree in computer science, information technology, information systems, data analytics, or a related area, or an equivalent combination of education and experience.
  • Five to seven years in data engineering or software engineering, including at least two years building and deploying cloud data platforms; GCP experience is preferred.
  • Strong command of SQL, Java, and Python.
  • Hands-on experience designing and deploying cloud data pipelines with GCP services such as BigQuery, Dataflow, and DataProc.
  • Understanding of service-oriented architecture and microservices.
  • Experience with relational databases such as PostgreSQL and MySQL, NoSQL databases, and columnar databases such as BigQuery.
  • Knowledge of cloud data governance frameworks, encryption, and data masking.
  • Familiarity with CI/CD pipelines, infrastructure as code tools such as Terraform and Tekton, and automation frameworks.
  • Strong analytical and problem-solving abilities, including troubleshooting complex data platform and microservices issues.
  • Experience monitoring and optimizing costs and compute resources for GCP services such as BigQuery, Dataflow, Cloud Run, and DataProc.
  • Interest in data, innovation, and ongoing learning.

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