Senior Product Manager — Energy Products & Quoting · vacancy
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.accounts — 27,640,145 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 strategy, vision, and roadmap for Energy Products & Quoting, ensuring it supports ENSEK’s customer commitments, international expansion, and the industry’s shift toward flexible and dynamic pricing.”
What is the tariff portfolio mix — Fixed vs Variable vs Green by account count and revenue?
bar chart“Lead the evolution of the platform’s product modelling and quoting capabilities to expand support for modern energy propositions: time-of-use tariffs, multi-rate structures, export products, agile pricing, and bundled propositions.”
Which single tariff carries the most accounts and estimated revenue — where is the concentration and switching risk?
bar chart“Champion the integration of AI across the product area, from intelligent product recommendations and pricing optimisation to AI-assisted catalogue management.”
What is green tariff penetration by customer segment — where is is_green adoption highest and lowest?
table“Drive international product capability, ensuring energy products and quoting can be localised and configured for new regulatory markets without bespoke development.”
Which active accounts have est_annual_kwh above 15,000 — the genuine heavy users and their tariff type?
deviation“Guide stakeholders through product lifecycle decisions, using evidence to determine when to invest further, pivot, or retire products and features.”
What is the consumption and revenue profile of the off-peak / time-of-use tariff population — EV Boost and Smart Saver accounts?
tableThe 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.accounts (27,640,145 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. Representative ENSEK-style operational dataset (480 accounts · 728 meters · 5,676 monthly consumption records). Schema mirrors my_db.ensek_demo.accounts/meters/consumption_monthly. Seed 20260609 — reproducible. No real ENSEK or customer data. Live server-side path: my_db.ensek_demo.accounts (27.6M rows). Dormant until operator provisions MOTHERDUCK_TOKEN.
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.