Energy meter-to-cash billing PM
Here's what AI can do for this role — and what still needs a human. Built straight from ENSEK's own job advert, running live on my_db.ensek_demo.ofgem_price_cap — 16 real rows via MotherDuck (DuckDB). Not a slide about AI. The job, getting done.
Every line on the left is lifted from ENSEK's actual job ad. If a card lacks a harvested JD line, it is omitted. On the right is the AI doing it — with eligible cards running live against the warehouse and offline inspection clearly labelled in the workspace.
“Own the product vision and roadmap for the Billing domain, spanning bill calculation, adjustments, and emerging flexible energy revenue streams.”
How have Ofgem electricity and gas unit rates changed across all quarterly price cap periods?
bar chart“Work with senior stakeholders, industry and customers to translate operational billing requirements into a clear and prioritised product strategy.”
How have standing charges for electricity and gas changed across Ofgem price cap periods?
bar chart“Partner closely with other areas to ensure settlement and metering data, half-hourly flows, tariffs, and industry reconciliation all feed accurately into billing calculations.”
What is the full dual-fuel price cap picture — all unit rates and standing charges across every period?
table“Identify opportunities where AI, data and automation can improve billing accuracy and reduce exception handling.”
What is national energy consumption by sector and fuel type in the latest available year — which sectors drive demand?
table“Support client SLA obligations, audit requirements, and regulatory reporting in the billing area.”
How has national electricity and gas consumption trended since 2015 — is demand rising or falling?
bar chart“Maintain a prioritised, well-reasoned backlog ensuring your engineering team always has clear context on what they are building and why.”
What is the long-run average energy consumption profile by sector — which sectors have the most stable vs variable demand?
kpiThe honest other half. AI does the analysis; a person owns the decision — especially where regulation, fairness and accountability bite.
A plain-English question — the same one the job ad describes — is translated to SQL by the agentic backend.
Curated cards run server-side against MotherDuck when eligible. The workspace separately labels any local inspection path.
Runs against my_db.ensek_demo.ofgem_price_cap (16 rows declared by the manifest). No synthetic numbers.
Each figure carries a falsifier — recomputed from the result set, not a stored number, so it can't quietly drift.
It's the role getting done: curated questions run live server-side against the warehouse; local inspection is labelled inside the workspace.
Open the live workspace →Provenance. Live Ofgem price cap data: 16 quarterly rows, Oct 2022 to Jul 2026. Live DESNZ DUKES consumption: 502 annual rows from 1970. Schemas: my_db.ensek_demo.ofgem_price_cap + my_db.ensek_demo.energy_consumption. Offline degrade uses the same slices in-browser.
It's Sorted — I took ENSEK's job ads and didn't write a report on what AI could do. I built it. Get the rest sorted →
I'm trained on this proof and the real ENSEK: the Ignition meter-to-cash platform (seven modules), the move under Centrica in 2024, 7M+ energy accounts migrated for suppliers like British Gas and Utility Warehouse, and the Ofgem framing. Ask me how the Data Analyst function changes shape, or which open roles map to which Ignition module.