Sales operations analyst covering the UK and Ireland book. Territory design, TAM validation and master data governance.
Sole analyst on the desk, supporting 31 inside and field sellers across three verticals rebuilt in sequence: financial and retail services, Ireland corporate, and education.
Barcelona · LinkedIn
Coverage models fail on the data underneath them. I re-establish every figure against the primary statutory filing and correct it upstream, before it reaches quota setting.
Every claim carries an evidence tier:
| Tier | Meaning |
|---|---|
| FACT | Taken verbatim from a statutory filing |
| DERIVED | Calculated from verified figures, with the method stated |
| ESTIMATE | No statutory source exists |
What cannot be verified is logged as a gap, never inferred.
Every move reconciles before the case closes. Opening ties to closing, and my own corrections are held separately from forecast movement so the two can be audited independently.
Both repositories run on generated data. No client or employer information appears in either.
A corporate book being restructured inside a planning cycle, modelled in SQL.
- Eight-table schema with referential integrity, check constraints and validity dating
- Recursive CTE for group hierarchy resolution, with a cycle guard
- Case lifecycle analysis across a status funnel
- A TAM reconciliation that ties opening to closing or fails
- Twelve queries, eight PASS/FAIL integrity checks that run before any figure is published
The design decision worth noting: movement caused by cases is held in a separate column from movement caused by the forecast refresh, so one contributor's effect can be isolated from background change.
A coverage audit across four verticals, in Python.
- 400 accounts, 24 territories, 12 reps, 5 relational tables
- 31-point integrity check before any figure is reported
- Account health, TAM at risk, live bid exposure, rep-level before and after
Findings: 57.8% of accounts carried a hierarchy issue at baseline, £3.7M of TAM sat behind alignment problems, and 37% of correction cases were still stalled in the workflow. The bottleneck was the process, not only the data.
SQL · Python (pandas, matplotlib) · Advanced Excel · Tableau · R
Salesforce (12 years) · Microsoft Dynamics CRM · Companies House and statutory filings
Google Data Analytics Professional Certificate · Data Analysis with R Programming
Open to senior sales operations, revenue operations and territory planning roles. Remote or Barcelona.