Data Model Governance Lead

proda.ai; London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An established industry player is seeking a hands-on technical product leader to own and govern complex data models in the commercial real estate sector. This role combines product management, data architecture, and quality assurance to ensure the accuracy and evolution of the data schema. You will collaborate across teams and mentor junior analysts, while being the key liaison for clients, translating their needs into actionable insights. Join a dynamic team dedicated to revolutionizing real estate data management and help shape the future of data-driven decision-making in this vital industry.

Benefits

Private Healthcare

25 Days Holiday

Learning & Development Budget

Flexible Hours

Remote Policy

Qualifications

• 5+ years in data-modelling, data-warehousing, or analytics engineering.
• Proven ownership of complex data schemas with ≥3,000 variables.

Responsibilities

• Own the end-to-end data model and ensure accuracy and stability.
• Act as primary contact for clients on data-model questions and requests.

Skills

Data Modelling

SQL

Project Management

Data Warehousing

Communication Skills

Change Management

Tools

PostgreSQL

Git

Notion

Confluence

dbt

Job description

About PRODA

At PRODA (www.proda.ai) we are building software to unlock the full potential of Real Estate Data no matter the asset class, language or country of origin.

Our solution leverages the latest technologies to automatically capture, standardise and quality check Real Estate data into a single, clean source of truth. The goal is to empower real estate professionals to quickly gain actionable insights from their data and to enable the effective use of software.

Despite being the biggest asset class in the world, the commercial real estate industry relies on excel & PDF “Rent Roll” documents as the primary means of exchanging data. These files are frequently inconsistent, inaccurate, and inaccessible, posing challenges for real estate professionals. Through automated data capture, standardisation, and analysis, PRODA can:

• Build repositories of clean, standardised data
• Increase process efficiency, through faster report generation
• Improve data-driven decision making, by ensuring the accuracy of input data

Furthermore, PRODA easily integrates with existing systems, to ensure consistent dataflow through the whole organisation.

Founded in 2017 by Charles and Peter, two former Real Estate Finance Investment professionals, PRODA is now 70 people strong, and is looking to expand further in 2025.

PRODA has a strong globally recognised client base with an even better sales pipeline. We are looking for you to join us on our exciting journey delivering best-in-class software to the Commercial Real Estate market globally.

Reporting Line

Reports to Chief Technology Officer (CTO) and works cross‑functionally with Engineering, Data, Product and Client Success teams.

Role Summary

PRODA’s rent‑roll data model underpins every product experience and client deliverable. You will become the single point of ownership for our four‑tier schema (with thousands of variables) and the hundreds of standardisation & validation rules built on top of it. Your mission is to safeguard accuracy, steer evolution, and make the logic transparent to colleagues and clients – eliminating costly regressions while accelerating new feature delivery. You will fully immerse yourself in the business logic behind every standardisation, validation and schema decision, and become a trusted liaison for clients, ensuring their evolving needs and customisation requests are met without compromising stability.

This is a hands‑on, technical product leadership role: part product manager, part data architect, part QA lead. You will deep‑dive into complex domain logic (e.g. German CPI threshold-based indexation vs. UK upward‑only rent reviews), orchestrate change across squads, and mentor a small team of data analysts.

Key Responsibilities

1. Data‑Model Ownership & Governance
   • Act as Product Owner for the multiple variable schemas – roadmap, backlog, versioning, deprecation.
   • Own the end‑to‑end data model. Hold complete functional knowledge of every variable family, rule set and API schema, and personally approve or delegate (with documented sign‑off) all changes. Establish processes, guardrails and dashboards so junior data engineers can execute safely, but ultimate responsibility for accuracy and stability remains with you.
   • Define and enforce standards (naming, data types, units, currencies, localisation, documentation).
   • Influence the product roadmap to support clients' needs that are sometimes conflicting.
2. Standardisation Logic Stewardship
   • Catalogue, document and continuously improve >400 business‑logic rules that infer & transform variables.
   • Design best‑practice patterns for multi‑column inference, currency/unit conversions, occupancy calculations, etc.
3. Validation & Quality Assurance
   • Own the library of validation checks; introduce contract tests and thresholds to catch edge cases early.
   • Implement automated regression‑test suites covering historic client files before every release.
4. Change Management & Release Coordination
   • Perform impact analysis & sign‑off for all schema / rule edits, balancing refactoring of legacy logic with a strict zero‑regression mandate.
   • Maintain clear change‑logs and communicate upcoming impacts to internal teams & clients.
5. Project, Client & Stakeholder Management
   • Act as primary point of contact for clients on data‑model questions; gather feedback and communicate upcoming changes. Translate client requests – including bespoke data‑model configurations – into well‑scoped tickets for junior data team members; set priorities & timelines.
   • Liaise with engineering squads to align API schemas and downstream data contracts.
6. Documentation & Enablement
   • Build living documentation (Notion / Confluence / dbt docs) & training materials explaining key rules with examples.
   • Run onboarding sessions for new joiners and knowledge‑share workshops for client‑facing teams.
7. Team Leadership
   • Line‑manage 2‑3 Data Analysts / Associate Data Engineers; coach on best practices & review their work.

Example Success Metrics (first 12 months)

KPI

Target

Regression incidents detected post‑release

≤1 per quarter

Turnaround time for approved client rule requests

<5 business days

% historic files covered by automated regression tests

>95%

Internal NPS on documentation usefulness

>8 /10

Client satisfaction with data accuracy (CSAT)

>80%

Required Skills & Experience

• 5+ years in data‑modelling, data‑warehousing, analytics engineering or similar product‑data roles.
• Proven ownership of large, evolving data schemas with complex business logic (≥3,000 variables).
• Hands‑on SQL (PostgreSQL preferred) and familiarity with modern ELT / dbt‑style modelling patterns.
• Strong understanding of testing frameworks, CI/CD, regression‑testing, and version control (Git).
• Excellent project‑management skills – able to balance quick wins with long‑term architecture.
• Clear written & verbal communication; able to translate tech details for non‑technical stakeholders.

Desirable

• Experience in PropTech, FinTech or other multi‑currency, multi‑jurisdiction data domains.
• Familiarity with real‑estate concepts (rent rolls, indexation, CAM charges, lease events).
• Exposure to TypeScript/Python engineering environments and API schema design (OpenAPI/JSON‑Schema).
• Prior line‑management or mentoring of junior analysts/engineers.

Personal Attributes

• System thinker – sees the bigger picture and designs for scale & longevity.
• Detail‑obsessed – happily dives into edge cases and historical quirks until fully understood.
• Change champion – drives process improvements and brings people along the journey.
• Customer‑centric – balances technical correctness with user value & clarity.

Salary & Benefits

Competitive base + equity; benchmarked to London tech scale‑ups.
Private healthcare • 25 days holiday (+bank holidays) • L&D budget • Flexible hours & remote policy.
Exact package finalised based on experience & location.