Data Engineer ID89384

AgileEngine; Barcelona; Hybrid; EUR 85,000 - 120,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

AgileEngine is seeking a Data Engineer to design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, and SQL. You will build agentic workflows to automate data engineering tasks and integrate Databricks, GCP, BigQuery and Delta Lake environments.

The role emphasizes data quality, production readiness, and collaboration with business and platform teams. A strong background in ETL/ELT design and scalable data solutions is required.

Qualifications

• 5+ years of hands-on Databricks and PySpark experience.
• Advanced SQL and data-processing skills.
• Experience with GCP, especially BigQuery.
• Knowledge of Delta Lake and data lake architectures.
• Strong ETL/ELT, data pipeline design and data quality.

Responsibilities

• Design, develop and optimize scalable ETL/ELT pipelines using Databricks and PySpark.
• Build agentic workflows to automate data engineering and operations.
• Integrate Databricks, GCP, BigQuery and Delta Lake environments.
• Develop reusable data frameworks and components.
• Implement data quality checks, monitoring and production controls.
• Optimize PySpark/SQL workloads for performance and reliability.
• Participate in testing, deployment, and production support.
• Collaborate with business and platform teams to identify further automation.

Skills

Databricks

PySpark

SQL

GCP BigQuery

Delta Lake

ETL/ELT

Data pipelines

Data quality

Troubleshooting

Software engineering practices

Tools

BigQuery

Delta Lake

Databricks

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.

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.

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.

PERKS AND BENEFITS

• Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
• Exciting projects: Modern solutions with Fortune 500 and top product companies.
• Flextime: Flexible schedule with remote and office options.

Meet Our Recruitment Process

Application -> Coding Challenge -> Video Interview -> Technical Interview or Hiring Manager Interview

Each step helps us understand your skills and overall fit.

If it's a match, you'll receive an offer.