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Senior Databricks Data Engineer


Job Location:

Bucharest - Romania

Monthly Salary: Not provided by the employer
Posted: 13 October 2026 (6 days ago)
Application Deadline: 10 January 2027
Vacancies: 1 Vacancy

Job Summary

Senior Databricks Data Engineer / DWH Developer

Location: Hybrid
Workload: 100% / Full-Time
Languages: English

Position Summary

We are seeking a Senior Databricks Data Engineer / DWH Developer for a 1-year this role you will design build and optimize enterprise-scale data pipelines and data warehousing solutions leveraging modern cloud technologies and Delta Lake.
You will work closely with cross-functional data teams to transform complex data streams into reliable high-performance analytical assets for business decision-making.

Key Responsibilities & Deliverables
  • ETL Pipeline Development: Architect and maintain robust scalable ETL/ELT pipelines for batch and real-time processing using PySpark or Scala.
  • Lakehouse Implementation: Design and optimize data storage and processing frameworks utilizing Databricks and Delta Lake architectures.
  • Data Warehousing & Modeling: Build production-grade data models (star schema snowflake Data Vault) and optimize query performance across analytical platforms.
  • Cloud Infrastructure Integration: Deploy and manage data solutions natively within a cloud environment (AWS Azure or GCP).
  • Performance Tuning & Monitoring: Monitor pipeline stability optimize Spark job performance reduce compute costs and resolve data quality issues.
Qualifications & Skills
Required
  • Databricks Expertise: Strong practical experience developing production data solutions on the Databricks platform.
  • Programming Languages: Proficiency in PySpark and/or Scala alongside strong advanced SQL skills.
  • Data Lake & Storage: Deep understanding of Delta Lake transaction layer and storage optimizations (ACID transactions time travel Z-Ordering).
  • Core Engineering: Proven background in traditional and modern Data Warehousing (DWH) and end-to-end ETL/ELT development.
  • Cloud Platforms: Solid working knowledge of cloud-native data architectures (Azure Data Factory/ADLS AWS Glue/S3 or GCP equivalents).
Preferred / Nice-to-Have
  • Experience with CI/CD for Databricks (Databricks Asset Bundles Terraform GitHub Actions).
  • Exposure to workflow orchestration tools (Airflow Azure Data Factory Databricks Workflows).
  • Relevant Databricks or Cloud certifications (e.g. Databricks Certified Data Engineer Professional).