Data Engineer

About MegaCharge

MegaCharge is an energy company specialized in delivering sustainable and cost-effective charging solutions for the transport industry. We are currently developing an Energy Management System (EMS) that seamlessly connects batteries, chargers, solar, and the grid for heavy-duty e-trucks.

We believe in transparency and customer support through data, values that inspired us to design and launch this project. Our EMS will enable fleet electrification at scale by intelligently managing energy flows, costs, and market interactions in real time.

As a clean-tech scale-up, we combine cutting-edge R&D (optimization, digital twins, and energy market intelligence) with solid software engineering (real-time data pipelines, cloud deployment, and user-friendly dashboards).

Core Mission

As a Data Engineer at MegaCharge, your mission is to build the data backbone of our EMS. Every optimization, dashboard and customer insight depends on data that is complete, timely and trustworthy. You make sure it is.

You will work in close partnership with our Software Engineer, our R&D team. You won’t need to invent optimization algorithms or build operator interfaces. Instead, you design and run the pipelines, storage and data models that feed them: from device and market data coming in, to clean, well-documented datasets going out.

Your input will be valued at every stage. You will actively influence how our data architecture evolves and collaborate across departments, from hardware and operations to R&D, to make sure every perspective is reflected in a robust, sustainable data platform.

Key Responsibilities

• Data Pipelines: Design, build and maintain batch and streaming pipelines that ingest data from chargers, batteries, solar installations, grid and energy market APIs.
• Time Series Data Platform: Model, store and serve high-volume time series data efficiently for both real-time use and historical analysis.
• Data Modeling & Schemas: Maintain clean, versioned schemas and data contracts for downstream use by the EMS backend, R&D and dashboards.
• Data Quality & Reliability: Implement validation, anomaly and gap detection, retries and alerting, so that faulty or missing data is caught early.
• Monitoring & Observability: Monitor pipeline health, latency and costs. Provide the data layer for monitoring dashboards (e.g. Grafana).
• Traceability & Governance: Ensure lineage, structured logging and auditability of data flows, in line with our cybersecurity and IAM principles.
• Cloud Infrastructure: Build and manage our cloud data stack in coordination with the cloud team, with a focus on performance and cost efficiency.
• Collaboration with R&D: Prepare datasets and feature pipelines for optimization, forecasting and ML models, and support their move into production.
• Documentation & Testing: Produce clear architecture and data documentation, and maintain code quality through tests and GitLab CI/CD workflows.