Description

Data & Systems Manager

Reports to: CTO

Role Purpose

The Data & Systems Manager owns the organisation's data architecture and the internal tools that let non-technical colleagues work with it safely. The role spans two closely linked responsibilities: designing and evolving the underlying data model (currently migrating from Airtable to Neo4j, a relational database to a graph database), and building the full-stack internal web applications, using Vite on the frontend and FastAPI on the backend, that consultants and analysts use for their day-to-day work.

The core problem this role solves is complexity: a single supply chain update might touch ten or more underlying tables or nodes, and analysts and consultants shouldn't need to think like database administrators to make that change correctly. The web tools this role builds translate intuitive, drag-and-drop interactions (think assembling supply chains as the entity boxes to drag-and-drop) into the correct, validated writes across the data model. Business logic and data quality rules are enforced in the tool itself, air-gapping analysts and consultants from the raw database and keeping bad data out before it ever lands.

AI is used in two ways: inside the tools themselves (e.g. suggesting linked standards, automating repetitive data tasks for end users) and in how this role works (using AI to accelerate full-stack development and ETL scripting). Strong, hands-on full-stack capability, not just data modelling, is core to the job.

Key Responsibilities

1. Data Architecture & Graph Modelling

• Design and maintain the organisation's data model, currently transitioning from Airtable to Neo4j as part of a CTO-led migration.
• Model entities, relationships, and supply chain structures in a way that is scalable, queryable, and reflects real-world business logic.
• Map existing relational data structures, identify redundancies and inconsistencies, and plan how they translate into the graph model.
• Ensure the data model can support both current use cases and the internal tools built on top of it.

2. Internal Tool Development

• Design and build full-stack internal web tools (Vite frontend, FastAPI backend) that let analysts and consultants create and edit supply chains and related data through intuitive, drag-and-drop interfaces rather than direct database access.
• Enforce business logic and data validation within the tools themselves, so a single guided action in the interface safely updates the correct underlying tables or graph nodes.
• Own ETL and data automation end-to-end, using AI to accelerate scripting and reduce manual, repetitive data processing.
• Build AI-assisted features into the tools, such as suggested linked standards and automation of repetitive data-entry tasks for end users.
• Continuously improve tool usability based on how analysts and consultants actually work, reducing the need for users to understand the underlying data architecture.

3. Cross-Team Collaboration

• Work closely with data analysts and consultants as the primary users of the internal tools, gathering feedback and translating their workflows into product decisions.
• Collaborate with the wider digital team on integration points, shared infrastructure, and alignment with client-facing platforms.
• Support the CTO-led Airtable-to-Neo4j migration from the data and tooling side, ensuring internal tools remain functional and reliable throughout the transition.
• Anticipate future data and tooling needs as the organisation's products and datasets evolve.

Essential Skills & Experience

• Strong full-stack development experience, ideally with Vite (or similar modern frontend tooling) and FastAPI (or a comparable Python web framework).
• Solid Python skills, including using Python for ETL, scripting, and automation.
• Experience with graph databases (Neo4j preferred) and/or relational database design, with the ability to work confidently across both during a migration.
• Experience designing tools or interfaces that abstract away complex backend logic for non-technical users — e.g. forms, validation layers, guided workflows.
• Comfort using AI tools/APIs to accelerate development and automate repetitive data tasks.
• Ability to translate business and analyst workflows (e.g. building a supply chain) into correct, validated data operations across a multi-table or graph structure.
• Strong communication skills, with the ability to gather requirements from non-technical colleagues (analysts, consultants) and turn them into practical tooling.

Desirable Skills & Experience

• Familiarity with Airtable or other relational databases. Neo4j or other graph database technologies is an advantage.
• Experience with ESG, supply chain, or raw materials-related datasets is an advantage, though not required — deep domain knowledge sits primarily with the analyst team.
• Experience building AI-assisted features such as recommendation/suggestion systems or automated classification.
• Understanding of data privacy, data residency, and security considerations relevant to internal tooling.

Success Measures

• Internal tools reliably keep analysts and consultants out of direct, multi-table database edits while still enabling complex changes (e.g. supply chain builds) confidently and correctly.
• Successful migration of the data backend from Airtable to Neo4j, delivered in partnership with the CTO with minimal disruption to end users.
• Reduced manual data processing through AI-assisted ETL and automation.
• High adoption and satisfaction among analysts and consultants using the internal tools.
• Data architecture and tooling scale cleanly as datasets, products, and client needs evolve.

[BT1]Individual contributor is dependent on whether a full-stack engineer can be found. This could also be a pair of: Frontend and Backend engineers/developers.