Data Engineer (Spark)

Addepto is a leading AI consulting and data engineering company that builds scalable, ROI-focused AI solutions for some of the world's largest enterprises and pioneering startups, including Rolls Royce, Continental, Porsche, ABB, and WGU. The company focuses exclusively on Artificial Intelligence and Big Data, helping organizations unlock the full potential of their data through systems designed for measurable business impact and long-term growth.

Addepto has developed its own product, ContextClue, and actively contributes open-source solutions to the AI community. It has been recognized by Forbes as one of the top 10 AI consulting companies worldwide.

As part of KMS Technology, a US-based global technology group, Addepto combines deep AI specialization with enterprise-scale delivery capabilities, enabling clients to move from AI experimentation to production impact securely and at scale.

• At least 3 years of commercial experience implementing, developing, or maintaining Big Data systems, data governance and data management processes.
• Strong programming skills in Python (or Java/Scala): writing clean code, OOP design.
• Hands-on experience with Big Data technologies like Spark, Cloudera, Kafka, Data Platform, Airflow, NiFi, Docker, and Iceberg.
• Excellent understanding of dimensional data and data modeling techniques.
• Experience implementing and deploying solutions in cloud environments.
• Consulting experience with excellent communication and client management skills, including prior experience directly interacting with clients as a consultant.
• Ability to work independently and take ownership of project deliverables.
• Fluent English (at least C1 level).
• Bachelor’s degree in technical or mathematical studies.

Nice to have:

• Experience with an MLOps framework such as Kubeflow or MLFlow.
• Familiarity with Databricks and/or dbt.

• Develop and maintain a high-performance data processing platform for automotive data, ensuring scalability and reliability.
• Design and implement data pipelines that process large volumes of data in both streaming and batch modes.
• Optimize data workflows to ensure efficient data ingestion, processing, and storage using technologies such as Spark, Cloudera, and Airflow.
• Work with data lake technologies (e.g., Iceberg) to manage structured and unstructured data efficiently.
• Collaborate with cross-functional teams to understand data requirements and ensure seamless integration of data sources.
• Monitor and troubleshoot the platform, ensuring high availability, performance, and accuracy of data processing.
• Leverage cloud services (AWS) for infrastructure management and scaling of processing workloads.
• Write and maintain high-quality Python (or Java/Scala) code for data processing tasks and automation.