Full Stack Data Engineer

🔥 1 hour ago

🇲🇽 Mexico – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Ford Motor Company

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1903

📦 Logistics

💼 Consulting

📣 Marketing

💰 Post-IPO Debt on 2023-08

Logistics • Consulting • Marketing

Ford Motor Company is a globally renowned automotive company based in the United States, established by Henry Ford. The company is committed to building a better world where every individual has the freedom to move and follow their dreams. Ford is dedicated to innovation, with a focus on services, experiences, and software alongside its traditional vehicle manufacturing. The company is actively involved in sustainability initiatives and aims to meet ambitious environmental targets. Ford values service, community impact, and strives to combine business success with social and environmental responsibility. With a rich history of over 121 years, Ford continues to adapt and lead in the evolving automotive landscape.

📋 Description

• Design, develop, and maintain scalable data ingestion and curation pipelines from diverse sources • Standardize, improve the quality of, and optimize data for analytical use • Contribute to seamless end-to-end development and reliable data flow from source to insight • Build and manage GCP data platforms using BigQuery, Dataflow, Pub/Sub, Cloud Functions, and related services • Implement and manage data governance policies, access controls, and security best practices • Use Astronomer and Terraform for workflow management and cloud infrastructure provisioning • Monitor and improve pipeline and storage performance, scalability, efficiency, resource utilization, and cost-effectiveness • Collaborate with data architects, application architects, service owners, and cross-functional teams on best practices, design patterns, and frameworks • Automate data platform processes to improve reliability, data quality, and operational efficiency • Communicate technical decisions to technical and non-technical stakeholders • Translate business requirements into data asset designs and efficient code • Document data engineering processes and promote knowledge sharing

🎯 Requirements

• Bachelor's degree in Computer Science, Information Technology, Information Systems, Data Analytics, Engineering, or a related field, or equivalent practical experience • 5 to 7 years of experience in Data Engineering or Software Engineering • Strong English skills • Minimum 2 years of hands-on experience building and deploying cloud-based data platforms, preferably on Google Cloud Platform (GCP) • Strong proficiency in SQL and Python • Experience with BigQuery, Dataflow, Dataproc, and cloud-based data processing technologies • Experience with Terraform, CI/CD pipelines, and automation frameworks • Knowledge of cloud security, data governance, and data quality best practices • Experience with relational databases such as PostgreSQL and MySQL, as well as NoSQL and columnar databases • Understanding of Service-Oriented Architecture (SOA) and microservices-based solutions • Knowledge of encryption and data masking techniques • Ability to monitor, troubleshoot, and optimize cloud workloads for performance, scalability, and cost efficiency • Preferred: experience with DBT, Dataform, Apache Airflow, Astronomer, microservices, API-based integrations, FinOps, and cloud cost optimization

🏖️ Benefits

• Equal opportunity employer committed to a diverse and inclusive workplace • Remote work arrangement • Opportunities to collaborate with cross-functional teams • Continuous learning about industry trends and emerging technologies • Knowledge sharing and documentation practices