Senior Data Engineer (Databricks)

Addepto is a leading AI consulting and data engineering company that builds scalable, ROI-focused AI solutions for large enterprises and pioneering startups. It operates within the AI and Big Data domains, serving clients such as Rolls Royce, Continental, and Porsche. The company is part of KMS Technology, a US-based global technology group, combining AI specialization with enterprise-scale delivery capabilities. Addepto has developed a product called ContextClue and contributes to open-source AI solutions.

• At least 5 years of commercial experience implementing, developing, or maintaining Big Data systems.
• Strong programming skills in Python including clean code writing and OOP design.
• Strong SQL skills, including performance tuning, query optimization, and experience with data warehousing solutions.
• Experience designing and implementing data governance and data management processes.
• Deep expertise in Big Data technologies, including Apache Airflow, Dagster, Databricks, Spark, and DBT.
• Experience implementing and deploying solutions in cloud environments, preferably Azure.
• Knowledge of building and deploying Power BI reports and dashboards for data visualization.
• Excellent understanding of dimensional data and data modeling techniques.
• Consulting experience with the ability to guide clients on architectural decisions, technology selection, and best practices.
• Ability to work independently and take ownership of project deliverables.
• Master’s or Ph.D. in Computer Science, Data Science, Mathematics, Physics, or a related field.

Nice to have:

• Experience with CI/CD processes and Kubernetes.
• Familiarity with Kafka messaging systems.
• Knowledge of Power BI, Dagster, and dbt tools.

• Design and optimize scalable data processing pipelines for both streaming and batch workloads using Big Data technologies such as Databricks, Apache Airflow, and Dagster.
• Architect and implement end-to-end data platforms, ensuring high availability, performance, and reliability.
• Lead the development of CI/CD and MLOps processes to automate deployments, monitoring, and model lifecycle management.
• Develop and maintain applications for aggregating, processing, and analyzing data from diverse sources, ensuring efficiency and scalability.
• Collaborate with Data Science teams on Machine Learning projects, including text/image analysis, feature engineering, and predictive model deployment.
• Design and manage complex data transformations using Databricks, DBT, and Apache Airflow, ensuring data integrity and consistency.
• Translate business requirements into scalable and efficient technical solutions while ensuring optimal performance and data quality.
• Ensure data security, compliance, and governance best practices are followed across all data pipelines.