Data Engineer
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL, and operationalize agentic workflows to automate data engineering processes.
- Dnipro, Dnipropetrovska oblast, Ukraine
- Posted Nov 10, 2026
INDUSTRYSoftware & InternetSaaS & Enterprise Software
Responsibilities
- 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 reusable frameworks and components to support new business requirements and datasets across multiple 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, troubleshooting, and ongoing enhancement of data and agentic solutions.
- Collaborate with business, data engineering, and platform teams to identify further automation opportunities.
Requirements
- Experience in a Data Engineering
- Experience in a AI/ML
Nice to have: AI/agentic workflows, AI agents, workflow automation, orchestration frameworks, reusable data engineering frameworks, productionizing AI-enabled solutions
Skills
DatabricksPySparkSQLGCPBigQueryDelta LakeETL/ELTdata pipeline designperformance optimizationdata qualitysoftware engineering practicesversion controltestingdeploymentmonitoringproduction supportDatabricksPySparkSQLGCPBigQueryDelta Lake
Benefits
- Mentorship
- TechTalks
- personalized growth roadmaps
- competitive compensation
- education budget
- fitness budget
- team activity budgets
- flextime
Full description
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