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hace 4 semanas

Naucalpan, México Ford Motor Company A tiempo completo

**Qualifications**- Minimum: Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).- Preferred: Master's degree in a related field.- 0-7 years of professional experience demonstrating capabilities in both software engineering and data engineering domains.- Strong proficiency in Python is essential, including experience with web frameworks (e.g., FastAPI, Flask) for API/backend development and libraries for data manipulation (e.g., Pandas).- Solid understanding and hands-on experience with **_BOTH _**SQL and NoSQL- Experience building and consuming RESTful APIs. Understanding of API design principles and best practices.- Experience with cloud platforms, particularly GCP. Familiarity with a mix of GCP services is ideal (e.g., BigQuery, Dataflow, Cloud Storage, Cloud Run, Pub/Sub). AWS/Azure experience is also valuable.- Proficiency with Git and version control workflows.- Experience with software development fundamentals: automated testing (unit, integration), CI/CD concepts, debugging.- Familiarity with data warehousing concepts, ETL/ELT patterns, and basic data modeling.- Excellent analytical and problem-solving skills applicable to both data and software challenges.- Strong communication and collaboration skills, comfortable working across different technical domains and with various stakeholders.**Responsibilities**:- Develop Data Platform: Design, build, and maintain scalable ETL/ELT data pipelines using Python and GCP services (e.g., Dataflow, BigQuery, Cloud Composer/Airflow) to process data from various sources. Manage and optimize data storage solutions (SQL, NoSQL, cloud storage, data warehouses/lakes) on GCP.- Ensure Data Quality & Reliability: Implement data quality checks, monitoring, and alerting for data pipelines and services. Contribute to data modeling and governance practices.- Cross-Functional Collaboration: Work closely with diverse teams to gather requirements, define technical solutions, and deliver features that meet both data and software engineering objectives.- Optimize & Troubleshoot: Proactively identify performance bottlenecks and cost inefficiencies in both data pipelines and software services. Troubleshoot and resolve issues across the stack.- Promote Best Practices: Champion best practices in software engineering (testing, code quality, CI/CD) and data engineering within the team.

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