Data & reporting analyst
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.energy_customers_360 — 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.
“Frame ambiguous business problems into structured analytical questions and define the right approach.”
Segments C and D carry the highest bills, widest retrofit gap and most saveable carbon despite being a minority of the book — campaign and support investment should concentrate here. What does the segment breakdown confirm?
bar chart“Lead the design and delivery of high impact analyses, ensuring outputs are statistically sound and decision-ready.”
How does energy cost and carbon vary across property wealth bands — from high net worth areas down to lower-value postcodes?
bar chart“Build scalable, reusable analytical datasets and dashboards that support cross team decision-making.”
Segment C pays a significant premium above the book average bill; Segment A sits below it — the CTE pattern isolates each segment's mean in one named subquery, joins the book baseline in a second, and makes the deviation explicit and reproducible.
bar chart“Identify and resolve complex data discrepancies, ensuring metric definitions remain consistent and trusted.”
How does estimated annual consumption rise as efficiency falls — what's the kWh and bill gradient across EPC bands?
bar chart“Communicate uncertainty, limitations, and trade-offs clearly to senior stakeholders.”
The top consumption quartile burns more than 3× the energy of the bottom quartile and carries the highest proportion of low-EPC homes — NTILE(4) window function proves the distribution is heavily skewed, confirming where demand management and efficiency investment should concentrate.
bar chart“Lead improvements to analytical processes, documentation, and validation practices.”
What's the decarbonisation opportunity by segment — current carbon versus what's saveable, and the reducible share?
bar chart“Frame ambiguous business problems into structured analytical questions and define the right approach.”
Which properties are the biggest retrofit opportunities — efficiency potential minus current efficiency of 30 points or more?
deviation“Lead the design and delivery of high impact analyses, ensuring outputs are statistically sound and decision-ready.”
Where should the retrofit campaign go first — which local authorities have the deepest average efficiency gap and most saveable carbon?
table“Build scalable, reusable analytical datasets and dashboards that support cross team decision-making.”
How exposed is the fuel-poverty-priority cohort (segment D) versus the rest of the book on bills, consumption and the efficiency gap?
kpi“Identify and resolve complex data discrepancies, ensuring metric definitions remain consistent and trusted.”
How are flood-exposed properties distributed across the book, and do they cluster with low-EPC homes — the vulnerability pinch-point?
bar chart“Communicate uncertainty, limitations, and trade-offs clearly to senior stakeholders.”
DAMA 6-dimension quality profile: how complete, unique and consistent is the dataset — the pre-analysis gate every senior analyst runs before publishing any claim.
kpi“Lead improvements to analytical processes, documentation, and validation practices.”
Hypothesis test: is the difference in mean annual bill between customer segments statistically meaningful — or noise? The answer-first output a senior analyst leads with.
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.energy_customers_360 (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. Offline degrade: labelled 5,000-row slice of the REAL ensek_demo.energy_customers_360 view — 27,640,145 national properties enriched with property valuations, area affluence (High net worth / Mid-market / Lower value), tenure, flood risk (flood_zone_rivers, area_flood_exposed). Live path queries the full 27.6M-row 360 view server-side — energy × wealth × vulnerability × climate across the whole country. No PII on eit
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.