› ENERGY & SUSTAINABILITY
Engineering inside the platforms behind the energy transition.
We embed senior engineers inside renewable-energy operators and research platforms. Azure, Django, Python, React — on top of the in-house stacks that already serve the assets, the customers and the analysts. Wind, solar, wave; community-scale to grid-scale; EU and US.
Two shapes of energy team we tend to be a good fit for.
Renewable-energy operators and energy-data research platforms have different cadences, but the operating model is the same: senior engineers, embedded, owning the data and platform layer alongside the in-house team.
Wind, solar and clean-energy operators digitising the asset base.
You operate generation assets and need engineering inside the data path — forecasting, monitoring, customer-facing portals, asset performance dashboards. We pick up the existing stack (Azure, Django, Python, React) and ship the parts the in-house team doesn't have capacity for.
TYPICAL: 1–5 YEAR ENGAGEMENTS · CLEAN-ENERGY · ASSET DATAEnergy research labs and market-data providers.
You produce energy intelligence — forecasts, scenarios, market models — and the audience is utilities, traders and policymakers. The product needs scientific rigour in the data, plus engineering rigour in the platform that delivers it. We've been in this space across wind/solar/wave research.
TYPICAL: 2+ YEAR ENGAGEMENTS · DATA-HEAVY · DOMAIN-AWAREThree product shapes we keep coming back to.
The work clusters into three categories — operator dashboards, customer-facing portals, and forecasting/research tools. Most engagements cross at least two of them.
Asset performance & monitoring dashboards.
Operator-facing dashboards over generation assets — wind farms, solar parks, distributed installations. Time-series ingestion, anomaly detection, alerting, role-based access for ops and engineering teams.
EXAMPLES: Time-series · Alerting · Asset viewsCustomer & member portals.
Portals for the people on the receiving end of clean-energy services — community-solar subscribers, asset-owners, partner utilities. Account, billing and service-tracking flows that respect the regulated context they're built inside.
EXAMPLES: Subscriber portal · Account · BillingForecasting, market data & research tools.
Platforms that turn meteorological and market data into the forecasts utilities and traders use. ETL discipline over messy upstream sources; visualisations that hold up under scientific scrutiny; access controls for paying audiences.
EXAMPLES: ETL · Forecasts · VisualisationsThe stack we ship inside. Whatever your engineering org already runs.
We don't impose a stack on energy clients — we plug into the one already there. Across our energy engagements that's usually some combination of Azure or AWS, Django or Rails, Python on the data side, React on the frontend.
PythonData · ETL
DjangoBackend
Ruby on RailsBackend
ReactFrontend
PostgreSQLDatabase
AzureInfra
AWSInfra
SnowflakeWarehouseProjects we've shipped in renewable energy.
Three engagements across EU and US — wind and solar consultancy, US community-scale clean energy, offshore-renewables research. Detailed case studies on request; the brief versions are below.
Modern frontends for an established European wind-and-solar consultancy. React and React Native delivery on top of an existing data and forecasting platform — built to put scientific output in front of utility, asset-manager and investor audiences.
Engineering capacity for a US clean-energy company committed to community solar and grid-scale renewables. Python / Django backend, React frontend, on Azure. Embedded with the in-house engineering organisation on the platform that runs the business.
Buoys — a research platform for an offshore-renewables organisation with strong ties to academic research. Time-series visualisation, geospatial views, the engineering layer behind data the research team needs to make sense of.
How an energy engagement runs — domain first, code second.
Energy products live or die on whether the engineers understand the data. The four steps below are consistent across renewables operators, research platforms and clean-energy ISVs.
Discovery — domain first, code second.
Energy products live or die on whether the engineers understand the data. We sit with the in-house ops, research or asset team, map the data sources, the cadence, the regulated parts of the workflow. Before any code, the data model is on paper.
WEEK 1–3Design with the operator and the analyst.
UX work runs alongside discovery — wireframes validated with the people who actually run the assets or read the forecasts. We design for the workflow, not for a SaaS template.
WEEK 2–6Build inside your stack.
We don't replatform. We integrate, extend, replace pieces in flight. Azure-native or AWS, Python / Django or Rails, React on the frontend. Whatever the in-house team already runs.
WEEK 6–[X]Ship, then keep going.
Phased rollout, regression suite the in-house team can run, monitoring tuned to the metrics ops actually cares about. Long engagement is by mutual choice.
ONGOINGOperating in renewables and need senior engineering inside the platform?
Tell us where the asset data is stuck, where the operator dashboard is breaking, where the forecasting pipeline is fragile. Thirty minutes is usually enough for both sides to know whether this is a good fit.