Data Engineer (Spark)

Addepto is a leading AI consulting and data engineering company that builds scalable, ROI-focused AI solutions for large enterprises and 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 contributes open-source solutions to the AI community. It is part of KMS Technology, a US-based global technology group, combining AI specialization with enterprise-scale delivery capabilities.

• 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), including writing clean code and OOP design.
• Hands-on experience with Big Data technologies such as Spark, Cloudera, Data Platform, Airflow, Kafka, 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 direct client interaction.
• 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 MLOps frameworks 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.