> Markdown version of [/jobs/ext/331933-data-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/331933-data-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analytics Engineer - **Company:** Orbital - **Location:** London, UK - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Application Layers, Software as a Service, Cloud Computing, Databases, Customer Data Management, Information Engineering, Data Infrastructure, Data Security, Key Management, PostgreSQL, Operational Data Store, SQL Databases, Data Streaming, Large Language Models, Snowflake, Low Latency, Data Analytics, Transactional Database, Looker Analytics, Databricks - **Published:** June 7, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=84cdcbeb4f6a271c ## About the Role Do you have experience in SaaS?, Do you have a Master's degree?, * 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 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 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 - we have 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, 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. ## Description * 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)