Most money apps talk at you. Cleo talks back ✨

We're not building another finance app. We're building the world's first AI financial assistant, one that actually understands your money and makes you better at it, and we're changing the world's relationship with money in the process 💰 For everyone, whatever their background or balance.

The proof: profitable, fast-growing, a unicorn with over $300M in ARR, and millions of people who now feel differently about their money.

We love original thinkers who challenge the status quo. We move fast, tell the truth, and there's nowhere to hide from good work here. If that excites you more than it scares you, you'll fit right in.

Follow us on LinkedIn for new features and the occasional roast 🔥

Meet the team 👥

Here at Cleo, we have ~200 Engineers organised into Pillars. ✨ Our stack spans a Ruby on Rails monolith, a single React Native (TypeScript) app frontend, Python for machine learning services, and PostgreSQL, all hosted on AWS and shipped through Kubernetes - backend goes out multiple times a week, app releases to Google and Apple at least weekly. To thrive here, our engineers proactively use our Engineering Principles to guide daily decisions, take ownership of problems beyond their own squad to drive broader impact, and actively drive their own learning, development, and growth.

About The Role ✍️

We are looking for a Lead Analytics Engineer, 6 month FTC to own the architecture and modelling for this work. You will partner with the teams responsible for our People systems, data platform, security and privacy, turning source data into trusted datasets that People Analytics and leadership can use. You’ll be responsible for:

• Design and build trusted data models for hiring/ATS data (Ashby) and talent/performance data (HiBob), including source mappings, keys, history, and reconciliation
• Strengthen the core employee model to reliably represent employees, jobs, teams, managers, and change over time
• Integrate engagement and survey data into the People data foundation in a consistent, reusable way
• Set clear model boundaries, grain, naming, tests, documentation, and data contracts across the People domain
• Identify and resolve duplicate, legacy, or conflicting People models without silently breaking downstream use
• Design a secure architecture for compensation, workforce cost, and other highly restricted fields, favoring masked, aggregated, or purpose-specific outputs over broad raw-data access
• Build a People data access-audit layer that attributes restricted queries to a named identity, retains evidence, and alerts owners to unauthorized or high-risk access patterns
• Partner with Security, Privacy, Finance, Reward, and business owners to agree access rules, disclosure controls, and acceptance criteria before restricted data is used
• Create a small set of governed workforce measures and datasets for leadership reporting, using native People-systems reporting where it already suffices
• Add tests, monitoring, failure routes, runbooks, and handover documentation so permanent owners can operate and evolve the work post-contract
• Enable People Analytics to focus on workforce and hiring questions by providing reliable, secure, and reusable data foundations