Data Analytics Engineer

Orbital Copilot is an AI assistant built exclusively for commercial real estate law, accelerating complex due diligence by up to 70% while delivering legal‑grade precision. We have recently raised 60 million dollars in Series B to support our expansion in the UK and US markets. Our clients include leading firms such as Goodwin and BCLP, and we empower legal teams to focus on sharp legal judgment and client service.

We’re looking for a Senior Data Analytics Engineer (Contract) to design and build the analytics foundations for a new greenfield product. No existing infrastructure; you will start from zero and leave behind a clean, well‑documented, extendable system. The core challenge is architectural: using an operational Postgres product database as the source of truth, extracting reliably as the schema evolves, standing up well‑structured operational data stores, and deciding where data lives, how it flows, and how it is queried. The analytics and visualisation layer – internal dashboards for engineering, product, CS teams, and customer‑facing usage reporting – is equally in scope. This is a senior role where you own architecture, tooling, and quality independently.

What you will be doing:

• Assess the Postgres product database and design an analytics architecture appropriate for our current scale without over‐engineering.
• Build reliable extraction pipelines from Postgres and other operational sources resilient to schema drift.
• Design and implement a well‐structured operational data store: clean schemas, stable marts, and a semantic layer trusted by teams.
• 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.
• Stand up internal analytics for engineering, product, CS, and leadership, and customer‐facing usage dashboards for law‐firm clients.
• Evaluate and recommend tooling for transformation, BI and semantic layer (e.g., Omni Analytics, Metabase) and cloud infrastructure.
• 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.
• Attend daily standup and work closely with the team, providing 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 made decisions on data flow, location, and access, not just executed a design handed to you.
• You have strong, hands‐on experience with Postgres as an operational data source: extraction patterns, handling schema drift, isolating analytics from application schema.
• You can independently set up a cloud data environment in AWS, data access, scheduled jobs, object storage, secrets, monitoring, and cost controls.
• You have built a data platform from scratch or near‐scratch before and can describe the decisions 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, 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 and will not reach for a full lakehouse or managed warehouse when something lighter serves the purpose.
• You write documentation that a coding agent can act on independently, not just a README for a human.

It would also be great if you have:

• Experience building customer‐facing or embedded analytics in a B2B SaaS product.
• Experience instrumenting AI/LLM usage: token counts, cost tracking, latency, and evaluation datasets.
• Familiarity with data residency requirements — strict UK/EU and US data residency obligations.
• Experience in ISO 27001 or SOC 2 compliant environments.
• Experience with multi‐tenant reporting, row‐level security, and customer data isolation.
• Startup or early‐stage background.
• Experience with transformation tooling such as dbt or equivalent code‐first approaches.

What this role is not:

• We are not looking for someone who will build an overblown lake in Snowflake or Databricks.
• We are not looking for a pure analytics or BI engineer who is great at SQL and dashboards but cannot stand up cloud infrastructure independently.
• And we are not looking for someone who needs a surrounding data team or close technical direction to operate.

The right person is a senior builder: self‐sufficient, architecturally minded, and pragmatic enough to build something clean that a coding agent can extend after they leave.

Security: Security is everyone’s responsibility. Team members should follow security policies, complete awareness training, and handle sensitive data with care in line with ISO 27001 standards. Reporting risks or incidents quickly helps maintain a strong culture of security and compliance.

Diversity and Inclusion: Orbital is committed to building a diverse and inclusive team and especially welcomes applications from people who are traditionally underrepresented in tech. Even if you don’t meet every single requirement, we’d still love to hear from you.

Compensation: This hiring range is a reasonable estimate of the base pay range for this position at the time of posting. Pay is based on several factors, which may include job‑related knowledge, skills, experience, and business requirements.