Software engineer - full stack (python & data platforms) (naucalpan de juárez)

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., Fast API, Flask) for API/backend development and libraries for data manipulation (e.g., Pandas). Solid understanding and hands-on experience with SQL and No SQL. 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 (e.g., Big Query, 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, Big Query, Cloud Composer/Airflow) to process data from various sources. Manage and optimize data storage solutions (SQL, No SQL, 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. #J-18808-Ljbffr