GCP certified Professional Data Engineer- Successfully designed and implemented data warehouses and ETL processes for over five years, delivering high-quality data solutions.
- 5+ years of complex SQL development experience- 5+ years of experience with programming languages such as Python, Java, or Apache Beam.
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- 5+ years of experience with GCP's underlying architecture and hands-on experience of crucial GCP services, especially those related to data processing (Batch/Real Time) leveraging Terraform, Big Query, Dataflow, Pub/Sub, Data form, astronomer, Data Fusion, DataProc, Pyspark, Cloud Composer/Air Flow, Cloud SQL, Compute Engine, Cloud Functions, Cloud Run, Cloud build and App Engine, alongside and storage including Cloud Storage- 5+ years of experience with DevOps tools such as Tekton, GitHub, Terraform, Docker.
- 5+ years of experience in designing, optimizing, and troubleshooting complex data pipelines.
- 3+ years of experience developing and deploying microservices architectures leveraging container orchestration frameworks- 3+ years of experience in designing pipelines and architectures for data processing.
- Passion and self-motivation to develop/experiment/implement state-of-the-art data engineering methods/techniques.
- Self-directed, work independently with mínimal supervision, and adapts to ambiguous environments.
- Evidence of a proactive problem-solving mindset and willingness to take the initiative.
- Strong prioritization, collaboration & coordination skills, and ability to simplify and communicate complex ideas with cross-functional teams and all levels of management.




- Proven ability to juggle multiple responsibilities and competing demands while maintaining a high level of productivity.
- Master's degree in computer science, software engineering, information systems, Data Engineering, or a related field.
- Data engineering or development experience gained in a regulated financial environment.
- Experience in coaching and mentoring Data Engineers- Project management tools like Atlassian JIRA-
- Experience with data security, governance, and compliance best practices in the cloud.
- Experience using data science concepts on production datasets to generate insights- Design and build production data engineering solutions on Google Cloud Platform (GCP) using services such as BigQuery, Dataflow, DataForm, Astronomer, Data Fusion, DataProc, Cloud Composer/Air Flow, Cloud SQL, Compute Engine, Cloud Functions, Cloud Run, Artifact Registry, GCP APIs, Cloud Build, App Engine, and real-time data streaming platforms like Apache Kafka and GCP Pub/Sub.
- Design new solutions to better serve AI/ML needs.
- Lead teams to expand our AI-enabled services.
- Partner with governance teams to tackle key business needs.
- Collaborate with stakeholders and cross-functional teams to gather and define data requirements and ensure alignment with business objectives.




- Partner with analytics teams to understand how value is created using data.
- Partner with central teams to leverage existing solutions to drive future products.
- Design and implement batch, real-time streaming, scalable, and fault-tolerant solutions for data ingestion, processing, and storage.
- Create insights into existing data to fuel the creation of new data products.
- Perform necessary data mapping, impact analysis for changes, root cause analysis, and data lineage activities, documenting information flows.
- Implement and champion an enterprise data governance model.
- Actively promote data protection, sharing, reuse, quality, and standards to ensure data integrity and confidentiality.
- Develop and maintain documentation for data engineering processes, standards, and best practices.
- Ensure knowledge transfer and ease of system maintenance.
- Utilize GCP monitoring and logging tools to proactively identify and address performance bottlenecks and system failures.
- Provide production support by addressing production issues as per SLAs.
- Optimize data workflows for performance, reliability, and cost-effectiveness on the GCP infrastructure.
- Work within an agile product team.
- Deliver code frequently using Test-Driven Development (TDD), continuous integration, and continuous deployment (CI/CD).
- Continuously enhance your domain knowledge.
- Stay current on the latest data engineering practices.
- Contribute to the company's technical direction while maintaining a customer-centric approach.

📌 Data Engineer (Naucalpan de Juárez)
🏢 Ford De México
📍 Naucalpan de Juárez