Data Engineer Id89384

Agileengine; Capital; Presencial; COP 364.275.000 - 496.738.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive compensation

Fortune 500 projects

Flextime

Descripción de la vacante

AgileEngine is seeking a Data Engineer with strong Databricks and GCP experience to design scalable ETL/ELT pipelines and operationalize agentic workflows for data automation.

You will integrate with existing Databricks, GCP, BigQuery, and Delta Lake environments; develop data processing solutions, and build reusable data engineering components for multiple use cases. Strong English and production-grade delivery are required.

Formación

• 5+ years hands-on Databricks and PySpark experience.
• Advanced SQL and data-processing skills.
• Hands-on GCP experience, esp. BigQuery.
• Delta Lake and lakehouse architectures knowledge.
• ETL/ELT design, performance optimization, data quality.
• Experience delivering production-grade data solutions.
• Strong analytical and troubleshooting abilities.
• Software engineering practices: testing, version control, deployment, monitoring.

Responsabilidades

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering processes.
• Integrate Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines to support new business requirements and datasets.
• Create reusable data engineering frameworks across use cases.
• Implement data quality checks, monitoring, and production controls.
• Optimize PySpark and SQL workloads for performance and scalability.
• Support testing, deployment, and productionizing of data solutions.
• Troubleshoot complex data and production issues; implement sustainable fixes.
• Collaborate with business, data engineering, and platform teams to find more automation opportunities.

Conocimientos

Databricks

PySpark

SQL

BigQuery

Delta Lake

ETL/ELT design

Data quality

Problem solving

CI/CD & monitoring

English

Descripción del empleo

Job Description

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.
• Upper-intermediate English level.

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.

Requirements

Strong hands-on experience with Databricks and PySpark. Advanced SQL and data-processing skills. Hands-on experience with Google Cloud Platform (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.