Data Engineer

Eleport is an EV charging point operator running public charging infrastructure across six CEE markets: Estonia, Latvia, Lithuania, Poland, Croatia, and Slovenia. They are building an in-house Analytics & Automations team with a greenfield data warehouse and no legacy infrastructure to maintain.

• 3+ years in data engineering, including delivery of at least one data warehouse or lakehouse from design to production.
• Strong expertise in SQL dimensional modelling, incremental loads, and query performance tuning.
• Production-grade Python skills for API ingestion with robustness features.
• Experience with modern cloud data platforms such as Microsoft Fabric, Azure Synapse, Databricks, Snowflake, or BigQuery.
• Proficiency with transformation-as-code and orchestration tools (e.g., dbt), version control, and CI/CD.
• Focus on data quality practices: tests, contracts, alerting.
• Understanding of data security, access controls, and governance for sensitive data.
• Working level English (minimum B2).
• Ability to operate independently, make decisions, document and explain trade-offs.

Nice to have:

• Hands-on experience with Microsoft Fabric components: OneLake, Lakehouse, Warehouse, Data Factory, Direct Lake.
• Experience with Power BI semantic models, including DAX, row-level security, incremental refresh.
• Familiarity with time-series or telemetry data (energy, IoT, utilities).
• Experience as the first or sole data engineer in an organization.
• Integrating operational SaaS APIs like Jira, CRM, ERP.
• Experience using LLM tooling inside pipelines and internal tools.

• Work with Head of Analytics & Automations to finalize warehouse and BI architecture.
• Audit source systems and their APIs; map entities and align definitions with business.
• Build initial production data pipelines.
• Deliver first trusted data layer for Finance, Operations, and Sales.
• Migrate manual reporting to automated pipelines.
• Implement data quality tests, monitoring, and alerting including source systems.
• Support BI dashboards and semantic layer for self-service reporting.
• Build ingestion pipelines for external market data.
• Automate repeatable analytical work and implement ML and AI-assisted workflows with IT.
• Collaborate directly with Operations, Finance, and Sales.

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