Data Engineering & Architecture
We build the pipelines and data models that bring ISO feeds, meter data, and market data together reliably, at scale — the foundation everything else depends on. When this layer is right, forecasting and reporting stop fighting the data and start using it.
Book a Consultation →Typical engagement deliverables
- Ingestion pipelines for ISO, market, and meter feeds
- Dimensional and semantic data models
- Data quality and observability framework
- Orchestration and CI/CD for data workloads
Technology & partner ecosystem
Interactive demos built with data engineering & architecture.
Live Power BI dashboards on synthetic data — click any to load it in place.
Fuel Supply Chain Analytics
Forward fuel position across sourcing, logistics, and inventory — synthetic data.
Refinery Supplier Performance
Seven years of supplier performance combining structured, semi-structured, and unstructured data — synthetic data.
ISO Day-Ahead & Real-Time Energy & Congestion
Energy and congestion arbitrage across market levels and individual locations — synthetic data.
Where data engineering & architecture shows up in energy.
A public-power utility took analytics off its billing system and cut a 3-hour report to 40 seconds.
A large municipal utility's billing system couldn't support analytics, and ad-hoc extracts were degrading it in production. We moved reporting onto an elastic cloud analytics layer with one governed source of truth.
A battery operator can see the nodal spread before it's obvious — and dispatch against it.
We built NodalAlpha — a battery-storage arbitrage engine that forecasts nodal basis spreads, ranks every ERCOT and CAISO node by forward opportunity, and schedules charge and discharge against a sized unit. Backtested on a full year of real ERCOT settlement prices, energy arbitrage only.
A PJM power producer's desk can ask "what's our net position next month?" — and get a grounded answer, not a report ticket.
An independent power producer's traders, analysts, and compliance team each live in a different system. A conversational layer, built on MCP, lets them ask their systems questions in plain language and get answers grounded in live data — not a static export.
A power trading business got a historic, attributable view of position and P&L — one version of the truth.
Market and credit risk, hedge effectiveness, and P&L attribution were scattered and hard to trust. We built an enterprise risk data warehouse — with VaR by Monte Carlo and full position and P&L attribution — as one consistent source across the organization.
A trading desk stopped reconciling every dashboard by hand and started trusting one governed source.
The desk had dashboards, but nobody trusted them — every number got re-checked against a private spreadsheet before anyone acted on it. We rebuilt the reporting on governed definitions and visible lineage that reconcile to the books, so the dashboard became the number people work from, not a second opinion.
A renewables portfolio replaced fragile spreadsheet tax-equity modeling with a rules-based engine analysts could trust.
Analysts were spending their time manipulating spreadsheets to model complex wind tax-equity deals instead of analyzing them. We built a rules-based engine that captures the deal terms and does the modeling — so the team could get back to decisions.
An oil & gas major built one risk repository for market, credit, and Dodd-Frank compliance.
Market risk, credit risk, and Dodd-Frank compliance lived in different places and rarely agreed. We led the design and delivery of a single risk data warehouse — one repository, one set of numbers the desk and the regulators both rely on.
How a data engineering & architecture engagement runs.
- 01
Assess
We start by understanding the current state — the data, the systems, and the real problem underneath the stated one.
- 02
Roadmap
We prioritize the work that will actually move the business, sequence it, and build the case for funding it.
- 03
Build
We deliver the pipelines, models, and reporting — working alongside your team, not in a silo.
- 04
Run
We hand over a solution your team can operate and evolve, with the enablement to keep it running after we leave.
Perspective on data engineering & architecture.
Why regulatory reporting is a data problem before it's a compliance one
Filing season turns into a fire drill when the data is gathered by hand every time. The fix isn't more reviewers — it's a reporting foundation that makes "prepare a filing" and "answer an audit request" the same fast, traceable operation.
Jul 22, 2026 · Read →Predicting outages starts with connecting OMS, AMI, and SCADA
Outage prediction gets pitched as a machine-learning problem. On most grids it's an integration problem first — the model is only as good as the OMS, AMI, and SCADA data you can actually bring together in time.
Jul 13, 2026 · Read →What breaks first when you move ISO market data to the cloud
The migration rarely fails on compute. It fails on the assumptions baked into how ISO feeds arrive, reconcile, and get trusted — here's what to check first.
Jun 24, 2026 · Read →