Senior Databricks Data Engineer

• Help design, build and continuously improve the clients online platform.
  
• Research, suggest and implement new technology solutions following best practices/standards.
  
• Take responsibility for the resiliency and availability of different products.
  
• Be a productive member of the team.
  
  
  

Requirements

• 5+ years of Data Engineering experience.
  
• 3+ years of hands-on Databricks experience.
  
• Play a key role in designing, building, and optimizing scalable data solutions on Databricks (AWS), enabling trusted data products, data governance, and AI-driven business capabilities.
  
• Strong data engineering expertise with hands-on experience in Databricks, cloud-based data platforms, data modeling, and modern software engineering practices.
  
• Hands-on Databricks Data Engineer who can independently design and implement data solutions, collaborate effectively with business and technical stakeholders, and contribute to delivering trusted, governed, and scalable data products.
  
• Develop and optimize ETL/ELT processes for large-scale enterprise data workloads.
  
• Implement data models and data products following Data Mesh and Medallion Architecture principles.
  
• Work with structured, semi-structured, and streaming data sources.
  
• Develop solutions using PySpark, SQL, and Databricks Workflows.
  
• Integrate and govern data using Unity Catalog.
  
• Support data quality, metadata management, lineage, and governance initiatives.
  
• Optimize data processing for performance, scalability, reliability, and cost efficiency.
  
• Collaborate with Data Architects, Product Owners, Data Governance teams, and Business Stakeholders.
  
• Contribute to CI/CD, automated testing, and deployment processes.
  
• Strong Python and PySpark development skills.
  
• Advanced SQL knowledge with experience in performance optimization.
  
• Experience with Delta Lake and Medallion Architecture.
  
• Experience with Databricks Unity Catalog.
  
• Experience building scalable ETL/ELT pipelines.
  
• Strong understanding of data modeling and data warehousing concepts.
  
• Experience with Git, CI/CD, and DevOps practices.
  
• Experience working in Agile delivery teams.
  
• Experience with Data Governance platforms and metadata management.
  
• Knowledge of Data Quality frameworks and monitoring solutions.
  
• Experience with Data Products and Data Mesh concepts.
  
• Experience with Databricks Workflows and Asset Bundles.
  
• Knowledge of AI, Machine Learning, or GenAI solutions.
  
• Experience working in regulated enterprise environments.
  
• Preferred Technology Stack: Databricks, PySpark, Python, SQL, Delta Lake, Unity Catalog, Git/GitLab, CI/CD, Data Quality Frameworks, Data Governance Tooling, REST APIs, Cloud Data Platforms.
  
  

Benefits

• A challenging, innovating environment.
  
• Opportunities for learning where needed.