Analytics Engineer

Le poste de Analytics Engineer (H/F)

? What We Do

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Founded in 2021 with the ambition to make cities cleaner and quieter, Electra is accelerating the shift to electric mobility by tackling the main barrier: charging.
We are building and operating a network of fast-charging stations (20 minutes on average) with a seamless and intuitive user experience.

Based in Paris, Lyon, Bordeaux, Nantes, Brussels, Madrid, Zurich, Vienna, Munich, Milan and Amsterdam, our teams aim to deploy 15,000 fast-charging points by 2030, contributing to Europe’s energy transition.

In just 5 years, we have:

• Deployed +750 stations and 4,000+ fast-charging points

• Opened offices in several European countries

• Raised over €1B from leading investors and institutions

• Joined French Tech Next40 and won European Scale-up of the Year 2024

• Grown to a team of 280+ talented people… and we’re just getting started

? Your Mission

Electra is deploying thousands of fast-charging points across Europe, and almost every decision behind them - where to build, how to price, how the network performs - depends on trustworthy data.

Our centralized data platform already powers reporting and analytics across Finance, Operations, Growth, Energy and more, with a broad catalog of data models and metrics. As we scale, we need one additional Analytics Engineer to help support our growth, tackle new data domains, and reinforce our semantic and context layer.

Your mission is to turn Electra's raw data into a reliable, self-service product the whole company can trust. You'll co-own the transformation layer end to end - clean data models, a consolidated semantic and context layer, and a reliable analytics agent - so that business and product teams all build on the same solid foundation.

We're open to a range of experience levels, from junior to senior: we care most about fundamentals and trajectory. You'll have a senior Analytics Engineer and the wider data team to learn from and grow with.

?‍?‍ Your Responsibilities

1. Co-own the dbt transformation layer: design and maintain data models across business domains, with clear ownership, tests and documentation in our hub-and-spoke architecture.

2. Extend and reinforce our semantic and context layer: keep metric definitions governed and consistent, expand coverage to new domains, and enrich the business context so every metric is defined once and interpreted the same way - across our BI tools and by our analytics agent.

3. Enhance our analytics agent: shape the semantic and context layer so an LLM-powered agent can answer business questions accurately, with the guardrails and evaluation that make its answers trustworthy.

4. Model new domains as the business grows: onboard sources like SAP, electricity costs and more into well-structured, reusable data models

5. Keep data trustworthy: harden our monitoring and alerting into robust metrics auditing and anomaly detection, so issues surface before stakeholders notice them.

6. Contribute to the data-platform foundations tied to analytics workloads: orchestration (Dagster) and ingestion (airbyte)

? Your ProfileTechnical skills

? sorted by priority (high to low)

Required

• Solid SQL and dbt foundations, and the drive to build clean, maintainable transformation layers (we expect depth to scale with your experience)

• Solid data modelling fundamentals: you design clean, reusable schemas and know how to turn messy sources into trustworthy marts

• Comfortable on a modern cloud data warehouse (Snowflake or equivalent) and the surrounding ELT stack

• A genuine care for metric consistency and clear definitions (hands-on experience designing or governing a semantic and context layer is a plus)

• You care about reliability and quality: data testing, monitoring, anomaly detection, version control and CI/CD

Nice-to-have

• Data-quality / observability tooling (Elementary, or similar)

• Orchestration (Dagster or similar) and ingestion tooling (Airbyte or similar)

• BI and self-service enablement (Omni, Metabase, or similar)

• Building or evaluating LLM-powered data tools (analytics agents, text-to-SQL), including guardrails and eval sets

• Cloud infrastructure exposure (AWS) and infrastructure-as-code

Mindset

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• Trust is the product. You treat correctness and reliability as the deliverable - a metric people can rely on beats a clever model no one trusts.

• Business-dri