Data Engineer ID89384

AgileEngine, LLC.; Puebla de Zaragoza; Presencial; MXN 550.000 - 850.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive compensation

A selection of exciting projects

Flextime

Descripción de la vacante

AgileEngine is seeking a Data Engineer with strong Databricks and GCP experience to design and optimize scalable ETL/ELT pipelines and productionize agentic data workflows. You will work across Databricks, PySpark, SQL, BigQuery, and Delta Lake to deliver robust data solutions for multiple business use cases.

The role emphasizes building reusable components, implementing data quality, and collaborating with cross-functional teams to automate and improve data operations.

Formación

• 5+ years of hands-on Databricks and PySpark experience.
• Advanced SQL and data-processing skills.
• GCP experience, particularly BigQuery.
• Experience with Delta Lake and modern data lake architectures.
• Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.
• Experience building reliable, scalable, production-grade data solutions.
• Strong analytical and troubleshooting skills.
• Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.

Responsabilidades

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
• Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines and processing solutions to support new business requirements and datasets.
• Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.
• Implement data quality checks, monitoring, validation, exception handling, and production controls.
• Optimize PySpark and SQL workloads for performance, reliability, and scalability.
• Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.
• Troubleshoot complex data and production issues and implement sustainable solutions.
• Collaborate with business, data engineering, and platform teams to identify further automation opportunities.

Conocimientos

Databricks

PySpark

SQL

BigQuery

Delta Lake

ETL/ELT

Data quality

Production data

Herramientas

BigQuery

Delta Lake

Descripción del empleo

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Data Engineer with strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.

WHAT YOU WILL DO

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
• Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines and processing solutions to support new business requirements and datasets.
• Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.
• Implement data quality checks, monitoring, validation, exception handling, and production controls.
• Optimize PySpark and SQL workloads for performance, reliability, and scalability.
• Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.
• Troubleshoot complex data and production issues and implement sustainable solutions.
• Collaborate with business, data engineering, and platform teams to identify further automation opportunities.

MUST HAVES

• 5+ years of strong hands-on experience with Databricks and PySpark.
• Advanced SQL and data-processing skills.
• Hands-on experience with GCP, particularly BigQuery.
• Experience with Delta Lake and modern data lake/lakehouse architectures.
• Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.
• Experience building reliable, scalable, production-grade data solutions.
• Strong analytical and troubleshooting skills.
• Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.

NICE TO HAVES

• Experience developing or integrating AI/agentic workflows, AI agents, or workflow automation solutions.
• Experience applying AI to automate data engineering, validation, troubleshooting, or operational processes.
• Familiarity with orchestration and automation frameworks.
• Experience developing reusable data engineering frameworks and platform components.
• Exposure to productionizing AI-enabled solutions with appropriate validation, monitoring, and human oversight.

PERKS AND BENEFITS

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.