About Eleport & the position
Eleport is an EV charging point operator (CPO) running public charging infrastructure across six CEE markets: Estonia, Latvia, Lithuania, Poland, Croatia and Slovenia and still growing! Every
quarter we open new sites, sign new partners and process hundreds of transactions across all of those markets. Due to rapid growth we are building an in-house Analytics & Automations team! This is a
greenfield build: no legacy warehouse to maintain, no on-prem servers, no ten-year-old ETL nobody dares to touch... You will help choose the stack and then build on it.
What you will do
At first:
• Work with the Head of A&A to finalize the warehouse and BI architecture: we have a shortlist and real
vendor pricing; you bring the engineering reality check
• Audit source systems and their APIs; map entities and agree shared definitions with the business
• Build the first production pipelines
After that:
• Deliver the first trusted data layer in the DWH: charging sessions, sites, contracts, costs and revenue
modelled so that Finance, Operations and Sales read the same numbers
• Migrate recurring manual reporting into automated pipelines
• Put data quality tests, monitoring and alerting in place - including for data quality in the source systems
themselves
And when settled:
• Support BI dashboards and a semantic layer for self-service reporting
• Build ingestion for external market data (competitor pricing, public charging registries)
• Automate repeatable analytical work and, with IT, put ML and AI-assisted workflows into production
You will work directly with Operations, Finance and Sales.
What we are looking for
Must have:
• 3+ years in data engineering, including at least one data warehouse or lakehouse you took from design to production: the scale matters less to us than having seen one through
• Strong SQL: dimensional modelling (star schema, slowly changing dimensions), incremental loads,
query performance tuning
• Production-grade Python: API ingestion with pagination, retries, rate limits and idempotent loads (not
notebook scripts)
• A modern cloud data platform in production: Microsoft Fabric, Azure Synapse, Databricks, Snowflake or
BigQuery
• Transformation-as-code and orchestration (dbt or equivalent), version control and CI/CD as a normal
way of working
• Data quality as part of the build (not an afterthought): tests, contracts, alerting
• English at working level (min. B2): our markets, colleagues and vendors are international
• Comfort operating without an engineering team around you: you decide, document, and are able to
explain the trade-offs afterwards
Nice to have, genuinely, not as a wish list:
• Hands-on Microsoft Fabric: OneLake, Lakehouse and Warehouse, Data Factory, Direct Lake, deployment
pipelines, and capacity/cost management (F-SKU sizing). We expect to build on Fabric, so this shortens
your ramp-up considerably
• Power BI semantic models: DAX, row-level security, incremental refresh
• Time-series or telemetry data - energy, IoT, metering, utilities
• Experience as the first or only data engineer in an organization
• Integrating operational SaaS APIs (Jira, CRM, ERP)
• Using LLM tooling inside pipelines and internal tools
What we do not expect:
• EV or energy domain knowledge - we will teach you that
• Big data at petabyte scale. Our volumes are modest; the difficulty here is breadth of sources and quality
of definitions, not throughput
• Building infrastructure from scratch. We buy proven SaaS and spend engineering time on what is specific
to Eleport
• Carrying this alone: architecture direction, priorities and the business decisions behind them sit with the
Head of A&A, who has built Eleport's Sales Operations environment from the scratch. You will have
someone to think with from day one and someone accountable for the calls you should not have to
make in your first month!
What we offer
• Salary of 3200-4200 EUR (EE) or 14000-18000 PLN (PL) gross per month on an employment contract.
The band is based on scope and experience; where you land in it is discussed openly at the offer stage
• Hybrid work; offices in city centers (Tallinn or Warsaw)
• Private heathcare in PL (Luxmed) or Wellness platform in EE (Stebby)
• A real greenfield: you help select the stack and then own it
• Direct influence on decisions. The models and reports built here feed pricing, capital allocation and
network expansion decisions at board level
• A small company where the distance between building something and seeing it used is short!