Senior Data Engineer / Tech Lead (Spark)

• At least 4 years of commercial experience in Data Engineering or Big Data projects.
• Strong programming skills in Python.
• Very good knowledge of SQL.
• Strong hands-on experience with Apache Spark and distributed data processing.
• Practical experience with workflow orchestration tools, preferably Apache Airflow.
• Experience with Apache Iceberg and/or modern lakehouse architectures.
• Experience working with Docker and Kubernetes.
• Good understanding of data modelling, data pipelines, and data-processing architectures.
• Experience working with relational databases and/or query engines such as PostgreSQL or Trino.
• General understanding of cloud environments and cloud-based data solutions.
• Ability to take ownership of technical solutions and contribute to architectural and technical decisions.
• Experience supporting other engineers through technical guidance, knowledge sharing, or mentoring.
• Ability to work independently and take ownership of project deliverables.
• Strong communication and collaboration skills.
• Fluent English (at least C1 level).
• Bachelor’s degree in technical or mathematical studies.

Nice to have:

• Experience with Kafka or other streaming technologies.
• Familiarity with modern data modelling, transformation, and ingestion tools (e.g. dbt, dlt, Airbyte).
• Experience with CI/CD and DevOps tools (e.g. GitHub Actions, ArgoCD).
• Familiarity with monitoring and visualization tools (e.g. Grafana, Power BI).
• Experience with Databricks and cloud-based data environments.

• Design, develop, and maintain scalable data pipelines using Python, SQL, Spark, and Airflow.
• Build and improve data-processing solutions based on Apache Iceberg and modern lakehouse architectures.
• Work with PyArrow, PyIceberg, PostgreSQL, and Trino.
• Take technical ownership of selected areas and contribute to technical and architectural decisions.
• Combine hands-on development with technical leadership and knowledge sharing within the team.
• Build and maintain containerized workloads using Docker and Kubernetes.
• Ensure data pipelines are scalable, reliable, maintainable, and production-ready.
• Collaborate with engineers and other stakeholders to understand business and technical requirements and translate them into effective solutions.
• Contribute to engineering best practices, including code quality, testing, CI/CD, and automation.
• Monitor, troubleshoot, and continuously improve existing data pipelines and platform components.
• Use modern development practices, including AI-assisted development, to improve engineering efficiency.

• Applications are collected and processed through Addepto’s official recruitment system; candidates are advised to verify that recruitment communications come from an official Addepto channel.