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AgileEngine

AgileEngine

Data Engineer ID89384

warsaw, PL现场永久制全职

发布于 2026年9月28日

此职位以 PL 发布

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.

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.

职位概览

工作类型

全职

所需技能

DatabricksPySparkAdvanced SQLGoogle Cloud Platform (GCP)BigQueryDelta LakeETL/ELT pipeline designData lake/lakehouse architecturesData quality, validation, and monitoringPerformance optimization for PySpark and SQL workloadsProductionizing data solutions (testing, deployment, production support)Troubleshooting and analytical problem solvingBuilding reusable data engineering frameworks and componentsAgentic/AI workflow integration and automationOrchestration and automation frameworks

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