Lead Analytics Engineer, Cleo AI

• Significant production Analytics Engineering experience, including technical leadership for a critical data domain
• Expert SQL and dbt, with strong judgement on source, staging, intermediate, mart and semantic-layer design
• Experience making architectural decisions about grain, history, slowly changing dimensions, incremental strategies, legacy boundaries and model contracts
• Experience leading a source-system migration or cutover while preserving history and reconciling outputs
• Strong data security and governance experience, including named access, least privilege, restricted schemas, masking or column controls, and auditable use of sensitive data
• Experience using warehouse access and query logs to create useful monitoring, investigation evidence or alerts
• Strong Git, PR review, testing, documentation and production-support habits
• The ability to explain technical choices and trade-offs clearly to Analytics, Security and non-technical business owners
• Evidence of leaving a complex data domain maintainable after handover
• Sound judgement and discretion when working with confidential employee, compensation and performance information

Desirable

• Contract: Up to six months, with a formal review at the end of month four
• Team: People Analytics, partnering across Data and People
• Cleo's People data spans core employee, recruitment, performance and engagement systems. We need a reliable foundation that connects these sources, preserves change over time and enables sensitive workforce information to be used safely
• We are looking for a Lead Analytics Engineer 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
• This is a time-bound assignment for someone who can make difficult technical decisions independently, work across Analytics, Security, Privacy and the People team, and leave the domain supportable after handover. The work is ordered by priority. New requests will replace an agreed deliverable rather than stack on top
• Designing and building trusted models for hiring and applicant-tracking data from Ashby, and talent and performance data from HiBob, including source mappings, keys, history and reconciliation
• Strengthening the core employee model so it reliably represents employees, jobs, teams, managers and change over time
• Bringing engagement and survey data into the People data foundation in a consistent and reusable way
• Setting clear model boundaries, grain, naming, tests, documentation and data contracts across the People domain
• Identifying and resolving duplicate, legacy or conflicting People models without silently breaking downstream use
• Designing a secure architecture for compensation, workforce cost and other highly restricted fields. Normal analytical use should rely on masked, aggregated or purpose-specific outputs rather than broad access to raw employee-level data
• Designing and building a People data access-audit layer that makes restricted queries attributable to a named identity, retains useful evidence, and alerts the right owner to agreed unauthorised or high-risk access patterns
• Working with Security, Privacy, Finance, Reward and business owners to agree access rules, disclosure controls and acceptance before restricted data is used
• Creating a small set of clear, governed workforce measures and datasets for leadership reporting, while using the native reporting in People systems where it already meets the need
• Adding tests, monitoring, failure routes, runbooks and handover evidence so permanent owners can operate and change the work after the contract ends
• Enabling People Analytics to focus on workforce and hiring questions by providing reliable, secure and reusable data foundations
• The role will partner with the teams responsible for source systems and ingestion, while owning the design and quality of the People data models and foundations delivered through the assignment