Lead Data Engineer, Orbital

• You have led or owned the architecture of a data platform: you have made the decisions on how data flows, where it lives, and how it is accessed, not just executed a design handed to you
• You have extracted from a live operational relational database (Postgres is ideal) and dealt with schema drift in production. This is the core of the technical challenge and the experience that matters most here
• You can independently set up a cloud data environment in AWS: data access, scheduled jobs, object storage, secrets, monitoring, and cost controls, without needing a platform team around you
• You have built a data platform from scratch or near-scratch before and can describe the decisions you made at the start
• You are strong in both data engineering (pipelines, infrastructure, operational data stores) and analytics engineering (semantic layer, metric definitions, clean queryable data models)
• You have deep SQL and data modelling capability: schema design, mart design, and semantic layer definition from scratch
• You understand BI and semantic-layer tooling (Omni Analytics, Looker, Metabase, Cube, or similar) and can make a justified recommendation
• You are pragmatic about tooling: you will not reach for a full lakehouse or managed warehouse when something lighter and more maintainable serves the purpose
• You write documentation that a coding agent can act on independently, not just a README for a human
• You are available to start by 1 July 2026
• Even if you don’t meet every single requirement, or if the right role isn’t listed yet, we’d still love to hear from you

Desirable

• 💡 Working at Orbital means joining a team that's reimagining how real estate transactions get done - moving fast, working collaboratively, and giving people the ownership to make a real impact from day one
• We're looking for a Senior Data Engineer with Analytics experience (Contract) to design and build the analytics foundations for a new greenfield product
• There is no existing infrastructure: no pipelines, no operational data store, no semantic layer. You are starting from zero and leaving behind something clean, well-documented, and extendable
• The core challenge is architectural: taking a live Postgres product database as the source of truth, understanding how to extract from it reliably as its schema evolves, standing up well-structured operational data stores, and making sound decisions about where data lives, how it flows, and how it is queried
• The analytics and visualisation layer, internal dashboards for engineering, product, and CS teams, plus customer-facing usage reporting for law firm clients, sits on top of those foundations and is equally in scope
• This is a Senior role because you are leading this build independently. Ciaran (Head of Product Engineering) is your day-to-day contact and sounding board, but he is not a data engineer and will not be directing the technical work. The architecture, the tooling decisions, and the quality of what gets built are yours to own
• This is an AI-first environment. We use Claude Code and coding agents extensively. . Good documentation here means documentation written for a coding agent: how to access systems, how to extend pipelines, why decisions were made. That is the handover standard
• Assess the Postgres product database and design an analytics architecture appropriate for our current scale: operational data stores, extraction strategy, schema isolation, and semantic layer, without over-engineering
• Build reliable extraction pipelines from Postgres and other operational sources that are resilient to schema drift and isolated from the application layer
• Design and implement a well-structured operational data store: clean schemas, stable marts, and a semantic layer that teams across the business can query and trust
• Define canonical business metrics: product usage, customer health, LLM token and cost telemetry, document volume, workflow adoption, latency, and engineering KPIs, and make them consistently available across the business
• Stand up internal analytics for engineering, product, CS, and leadership, and customer-facing usage dashboards for law firm clients showing their own usage and cost data
• Evaluate and recommend tooling for transformation, the BI and semantic layer (Omni Analytics is being evaluated alongside Metabase), and cloud infrastructure: bring your own experience and opinions
• Set up secure data access, scheduled jobs, object storage, secrets management, monitoring, and cost-aware infrastructure in AWS or Azure independently
• Establish data quality checks and pipeline observability from the start
• Write documentation for AI coding agents: how to access, understand, and extend the systems you build, with context on the decisions you made
• Attend daily standup and work closely with Ciaran throughout, with a clean handover at the end of the engagement
• Security is everyone’s responsibility at Orbital. We ask all team members to follow our security policies, complete regular awareness training, and handle sensitive data with care in line with ISO 27001 standards