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 startups, including clients like Rolls Royce, Continental, Porsche, ABB, and WGU. The company specializes exclusively in Artificial Intelligence and Big Data, developing its own product ContextClue and providing open-source solutions. Addepto is part of KMS Technology, a global technology group, combining AI specialization with enterprise-scale delivery capabilities.

• At least 5 years of commercial experience implementing, developing, or maintaining Big Data systems.
• Strong programming skills in Python, including clean code and OOP design.
• Strong SQL skills with performance tuning, query optimization, and experience with data warehousing solutions.
• Experience designing and implementing data governance and data management processes.
• Deep expertise with Big Data technologies including Apache Airflow, Dagster, Databricks, Spark, DBT, and modern data orchestration and transformation tools.
• Experience deploying solutions in cloud environments, preferably Azure.
• Knowledge of building and deploying Power BI reports and dashboards.
• Excellent understanding of dimensional data and data modeling techniques.
• Consulting experience with ability to guide clients on architectural decisions 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 related field.

Nice to have:

• Experience with CI/CD, Kubernetes, Kafka, and additional data orchestration tools.

• Design and optimize scalable data processing pipelines for streaming and batch workloads using Databricks, Apache Airflow, and Dagster.
• Architect and implement end-to-end data platforms ensuring availability, performance, and reliability.
• Lead development of CI/CD and MLOps processes for deployments and model lifecycle management.
• Develop applications for aggregating, processing, and analyzing diverse data sources.
• Collaborate with Data Science teams on Machine Learning projects including text and image analysis, feature engineering, and predictive model deployment.
• Design and manage complex data transformations using Databricks, DBT, and Apache Airflow.
• Translate business requirements into scalable, efficient technical solutions maintaining data quality.
• Ensure data security, compliance, and governance best practices across data pipelines.

Project examples:

• Design and develop a universal data platform for global aerospace companies using Azure and Databricks.
• Implement a data platform transformation for an energy management association using Databricks and Apache Spark/PySpark, including data catalog and governance features.
• Design data transformation and data ops pipelines for a global car manufacturer, handling real-time streaming and batch data, optimizing data cleaning and normalization processes.