Product Manager — Data Platform · 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 strategy, vision, and roadmap for the PDL as a governed data product — engaging external and internal consumers, the Analytics Chapter, and engineering leadership to define, validate, and continuously iterate the direction.”
How fresh is the meter-read pipeline — mean days since last read by meter technology?
bar chart“Own the commercial narrative for the PDL as an external product offer — defining what ENSEK’s data capability means to an energy retailer’s analytical team.”
How complete is the monthly consumption pipeline — accounts with ≥10 months of records vs sparse accounts?
bar chart“Make evidence-based prioritisation decisions, estimating the effort and value of roadmap items, using AI-powered estimation tools to improve accuracy, and discussing trade-offs transparently with stakeholders.”
What percentage of active accounts have a known EPC band — how complete is the enrichment pipeline?
kpi“Define and track key product outcomes — implementing dashboards for real-time performance visibility, and using data to drive continuous improvement in quality and user experience.”
Which accounts are high-consumption outliers — est_annual_kwh > 20,000 kWh flagged for data quality review?
deviation“Represent users confidently in internal discussions, advocating for research across all user types and leveraging AI-powered tools to accelerate insight generation at scale.”
What is the monthly data volume trend — total kWh ingested and record count by month across the platform?
bar chartThe 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.