Senior Databricks Data Engineer
Summary
A 1-year hybrid contract role in Bucharest where you design, build, and optimize enterprise-scale ETL/ELT pipelines and data warehouse/lakehouse solutions on Databricks and Delta Lake, using PySpark or Scala, advanced SQL, and cloud platforms (AWS, Azure, or GCP), with performance tuning and data quality work.
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 engagement. In 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).