Data Engineer (Athens)

As a Data Engineer, you play a central role in shaping and evolving our data landscape. You maintain a clear end‑to‑end overview of the various domain data models in the data warehouse, and you take ownership of extending and refining them.

• Within projects, you assess the impact of required changes, translate them into well‑structured models, define the right implementation approach, and align with the data engineering team.
• Together with your colleagues, you implement and deliver these changes hands‑on.
• As a data engineer, you design and build the ETL pipelines that ingest data from diverse source systems, transform it according to well‑defined business logic, and integrate it into the data warehouse or expose it on the central data platform.
• Data quality, observability, and reliability are essential principles in everything you deliver.
• You work with a mix of technologies — SQL databases such as MySQL and PostgreSQL, structured and semi‑structured formats like JSON and XML, monitoring solutions like Splunk, and integrations via APIs. Many of the datasets originate from technical domains such as Security, IT processes, CMDB, and ITSM.
• You maintain legacy ETL flows in on‑premises Microsoft data stack and assist in migrating them to modern Microsoft‑based ecosystem: Azure SQL databases, Microsoft Fabric, Azure Databricks.
• For monitoring and troubleshooting, you leverage tooling such as Azure Monitor, Log Analytics Workspace (LAW) and Dynatrace.

• Several years of experience as a data engineer, with strong expertise in building ETL flows.
• Solid knowledge of T‑SQL and hands‑on experience with SSMS.
• Strong understanding of data warehouse and modeling principles (e.g., Kimball, Inmon, Data Vault).
• Experience in documenting data models clearly and consistently. Experience and strong interest in Azure Databricks and Azure Data Factory.
• Proven knowledge of Python or another programming language relevant to data engineering.
• Familiarity with general IT principles such as DTAP, data center terminology, and best practices for reliable, production‑grade data pipelines.