Analytics Engineer

We are looking for an Analytics Engineer to join our data team. Your remit is to build and run a scalable, secure analytics platform that the whole of Ohme can trust, reporting into our Lead Analytics Engineer. 

This is a role defined by outcomes rather than tickets. The warehouse you shape will decide how quickly Ohme answers its questions, how much it costs to do so, and how confidently teams act on what the data says. You will take end to end ownership of our analytics pipeline from data lake to semantic layer, with real latitude over how that platform is built. 

• 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. 

• 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. 

• 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. 

Analytics Engineering at Ohme is evolving, so the path ahead is real rather than theoretical. The shape of it: 

• 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. From there the path runs towards Senior Analytics Engineer, with room to lean into technical depth (platform and architecture) or breadth (mentoring and leading through others).