> Markdown version of [/jobs/ext/3580353-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/3580353-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). --- # Analytics Engineer - **Company:** Ohme - **Location:** London, UK - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Code Review, Cursor, Dimensional Modeling, Python (Programming Language), Role-Based Access Control, Cloud Services, Standard Sql, Claude Code, Snowflake, Data Management, Dynamic Data, Terraform - **Published:** October 4, 2026 - **Apply:** https://startup.jobs/analytics-engineer-ohme-ev-com-10281806 ## About the Role * You think in models, not just queries. Strong SQL and a solid grasp of dimensional modelling (star schemas, facts and dimensions, slowly changing dimensions), and you can explain why a model is shaped the way it is. * You've run a warehouse with a budget in mind. Hands-on experience with Snowflake, including its security features (RBAC, column-level security, dynamic data masking, SSO or identity provider integration), and an instinct for where cost hides. * You build with AI tooling. Comfortable with tools such as Claude Code or Cursor, and curious about how they change analytics engineering workflows. * You know the semantic layer matters. Familiarity with LookML, Omni, the dbt Semantic Layer or similar, and an understanding that a warehouse is only as useful as the questions it can answer. * You connect technical choices to business outcomes. You care whether a model actually improves a decision, and you use that context to make trade-offs between cost, freshness, correctness and speed. * You communicate across altitudes. You can review a pull request with an engineer in the morning and explain to a non-technical stakeholder why their number changed in the afternoon, without losing precision in either. * You turn ambiguity into a plan. Given a loosely defined problem, you work out what matters, propose a direction and sequence the work, asking for input rather than permission. * You give and take feedback well. Code review is a conversation about getting to the best answer. You hold a high bar, explain your reasoning and change your mind when someone has a better argument. * You take ownership. When something breaks you want to understand why and stop it happening again, rather than waiting to be asked. * Experience with cloud data platforms, preferably AWS. Terraform and Python are a plus. ## Description * The Snowflake warehouse and the dbt project behind it: its data models, engineering standards and cost, shared with the Lead Analytics Engineer. * Data quality and reliability: how we test, monitor and alert, and how we respond when data is wrong or late. * Platform cost per charger: the metric that tells us whether the platform scales more cheaply than the business grows. * The warehouse as a product for its consumers: analysts, business users and the AI tooling that increasingly depends on clean, documented data. The Impact You'll Have: * Making Ohme's data cheaper to run than the business grows. Every data asset carries a visible cost and an owner, and you build the processes that find and remove waste. Success looks like Snowflake cost per charger falling while the fleet keeps expanding. * Making the warehouse the version of the truth people reach for. Well-modelled, well-tested and well-documented dbt assets mean fewer arguments about whose number is right, and more time spent on the decision itself. * Catching problems before anyone downstream does. Observability across spend, freshness and tests, so a stale dashboard or a broken model is something the data team tells the business about, not the other way round. * Raising the standard of how we build. Your code reviews and the engineering standards you help set shape how every model is written, not just the ones you write yourself. * Multiplying the team with AI. Routines, agents and workflows that take on the repetitive parts of analytics engineering, so the team's time goes on judgement rather than toil. * Being the person people come to. Analysts, engineers and business stakeholders know who to ask about the warehouse, and get a clear, useful answer., * First six months. Get the fundamentals right: security, cost visibility and reliable, well-reviewed models, while learning Ohme's data inside out. * Six to twelve months. Own the cost and quality outcomes end to end, with the latitude to decide how they are achieved. * Twelve to eighteen months. Become the point of contact for Ohme's analytics platform and shape the platform's architecture alongside the Lead Analytics Engineer. * Beyond. 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