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 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) with clean code and OOP design.
• Hands-on experience with Big Data technologies including 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.
• 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 large platform for processing automotive data in streaming and batch modes using Spark, Cloudera, Airflow, Iceberg, Python, and AWS.
• Design and develop a universal data platform for global aerospace companies using Azure and Databricks, including architecture and process design.
• Implement a centralized reporting platform for a US telecommunications company using BigQuery and Looker, integrating various CRMs and building executive reporting solutions.
• Develop and maintain high-performance data processing platforms ensuring scalability and reliability.
• Design and implement data pipelines processing large volumes of data in streaming and batch modes.
• Optimize data workflows for efficient ingestion, processing, and storage using Spark, Cloudera, and Airflow.
• Work with data lake technologies like Iceberg to manage structured and unstructured data.
• Collaborate with cross-functional teams to understand data requirements and integrate data sources.
• Monitor and troubleshoot platforms to ensure high availability, performance, and data accuracy.
• Leverage cloud services (AWS) for infrastructure management and scaling.
• Write and maintain high-quality Python (or Java/Scala) code for data processing and automation.