Technical Account Manager · 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.
“Provide expert guidance on platform capabilities, technical best practices, optimisation strategies, and technical roadmap planning.”
What is the account book health by region — active, pending and closed account counts for the portfolio the TAM presents to clients?
bar chart“Develop deep understanding of customer’s technical environment, architecture, workflows, and strategic technical initiatives.”
Which active accounts have churn_risk >= 7 — the at-risk list the TAM proactively manages with clients, with segment and current balance?
deviation“Collaborate with the customer on technical health continuously through usage metrics, performance data, error logs, API analytics, and system health indicators.”
What is the vulnerable PSR account load — count and percentage by segment and region (the regulatory flag portfolio the TAM must ensure client processes handle correctly)?
bar chart“Analyse customer usage patterns and provide optimisation recommendations to improve performance, reliability, scalability, or cost efficiency.”
What is the arrears exposure by customer segment — in-arrears account count and mean balance (the debt book the TAM monitors with the client's collections team)?
bar chart“Drive adoption of advanced features, APIs, integrations, and technical capabilities that deliver additional value.”
Show the top-20 highest-revenue active accounts — the TAM's strategic account list.
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.