Data Analytics Engineer

Responsibilities

• 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 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
• You should apply if
• You have led or owned the architecture of a data platform and 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 strong, hands-on experience working with Postgres as an operational data source: extraction patterns, handling schema drift, isolating analytics from application schema, and building on top of a live transactional database
• 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

Benefits

Additional Information

🚀 We're on a mission to make real estate transactions smarter, faster, and friction-free. 🏢 Real estate is the world's largest asset class , yet the legal processes and tools behind it remain slow, manual, and underinvested. Lawyers must review dense documents line by line and piece together information across silos, all while clients demand faster, more transparent due diligence. 🤖 That's where we come in. Orbital Copilot is the AI assistant built exclusively for commercial real estate law. Developed with former practicing real estate lawyers, it accelerates complex due diligence by up to 70% while delivering legal-grade precision. 💰 We've just raised a $60m Series B to accelerate our UK/US expansion. 🤝 We're trusted by leading firms like Goodwin and BCLP to remove the busywork so legal teams can focus on what they do best: applying sharp legal judgment, delivering standout client service, and getting deals over the line faster. 💡 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 Analytics Engineer (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. 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.

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