Senior Software Engineer - Data Platform

About Multiverse

Multiverse is the upskilling platform for AI and Tech adoption.

We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce.

Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.

In June 2022, we announced a $220 million Series D funding round co-led by Step Stone Group, Lightspeed Venture Partners and General Catalyst. With a post-money valuation of $1.7bn, the round makes us the UK’s first EdTech unicorn.

But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output.

Join Multiverse and power our mission to equip the workforce to win in the AI era.

We are looking for a Senior Data Engineer to join our Foundations team and create the technical foundations, including infrastructure, tools, and APIs that allow our entire company to access product data safely and efficiently.

This role moves beyond just building data pipelines. We are looking for a T‑shaped engineer
, essentially a backend or platform engineer with deep data proficiency. You will act as an enabler, creating the infrastructure, APIs and data models that allow our Tech teams, and Business teams access and use data efficiently in a self‑serve manner
.

As a member of the Foundations team, you will work closely with Infrastructure, Dev Ex, and Security engineers. Your goal is to ensure the data platform integrates seamlessly with our other internal tools and becomes a core pillar of our Internal Developer Platform (IDP).

• Tech Teams:
  Who need secure APIs to access data for their services and use MCPs (Model Context Protocols) to become more productive and build the right thing.
• Data Scientists & ML Engineers:
  Who need a stable and performant environment to build and deploy AI models.
• Business Users:
  Who need high-quality dashboards and secure API access to build their own automations and workflows without engineering bottlenecks.

• Take ownership of our data architecture. You will define schemas for new entities and refactor existing models to improve performance and clarity as the product evolves.

• Create APIs and connectors that allow users to access data easily without needing deep infrastructure knowledge.
• Enable the "Universal Data Layer" that powers our AI agents and internal services.

• Build features in our Internal Developer Platform that make it easy to deploy and manage AI models.
• Remove the friction between "training a model" and "running it in production."

• Automate security and compliance checks so that data is classified and safe by default.
• Replace manual approval gates with automated guardrails, ensuring speed without compromising safety.

• Encapsulate Business Logic:
  Design and develop services that wrap complex business logic into clean, reusable APIs.
• Simplify Data Consumption:
  Create a "Productised Data" layer, making it easy for non-technical stakeholders to pull high-fidelity reports and build dashboards without needing to understand the underlying raw tables.
• Refactor for Scale:
  Transition legacy data scripts and coupled domains into robust, version-controlled services that the entire company can rely on.

• A solid grasp of system design and data structures, coupled with foundational software engineering background. You write code that is tested, modular, and readable (we use Python, Type Script, and Go).
• Proficiency with cloud-native development (AWS or Azure) and containerisation (Kubernetes/Docker).
• Experience with Cloud Data Warehouses (Snowflake/Postgres), Data Warehouse API (Graph
  
  QL).
• Experience in data engineering, including building and maintaining data pipelines. You treat data pipelines like…