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., FastAPI, Flask) for API/backend development and libraries for data manipulation (e.g., Pandas).
Solid understanding and hands-on experience with 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 (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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