Principal Analytics Platform Engineer, tem

• Exceptional communicator: Equally at home with technical ICs and with the C-suite, can translate fluently between the two and make data strategy legible to anyone in the room
• Full-stack analytics depth: Hands-on understanding of the modern analytics stack, from ingestion and warehouse architecture through transformation (dbt), semantic layers, and BI delivery; the kind that comes from having built and operated each layer, not just having opinions about them
• Data standards that stick: A track record of establishing analytics standards the business actually adopted, repeatable analysis patterns, metric definitions, modeling conventions, data quality frameworks, and the communication skills to drive that adoption
• Self-serve data access experience: Has built or designed scalable data access capabilities, semantic layers, BI tooling, embedded analytics, or equivalent, and understands what makes them succeed or fail
• A clear POV on what good looks like: Can assess current state, identify the gaps, and articulate a credible roadmap without waiting for someone to define the destination
• Startup experience that counts: Demonstrable experience navigating analytics challenges at an early-stage company, has been in the messy early phase, knows what failure looks like, and has built a way out of it
• If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for

Desirable

• Tem is data-hungry
• The business runs on insight, and demand for accurate, accessible, trusted data has outpaced what any team operating tactically can sustain
• Analytics has become a bottleneck
• We're hiring an Analytics Platform Engineer to change that
• This is a director-level individual contributor role: the most senior IC in the analytics space, with no people management responsibilities and significant influence over how the whole company thinks about and uses data
• You will own the analytics platform end-to-end, from source ingestion through to the data surfaces teams actually use to make decisions
• That means setting the strategic direction, working closely with data engineers, analytics engineers, and analysts to bring it to life, and engaging directly with the C-suite on analytics strategy and delivery
• This is not a role for a strategist who hands off execution, or an executor who waits for direction
• At tem's current stage, both are needed from the same person
• The stack is modern and deliberately chosen: ClickHouse as our warehouse, dbt for transformation, DLT for ingestion, Apache Iceberg and Parquet for storage, Omni for BI delivery, and MCP tooling for data access
• You'll inherit something real and be expected to take it further
• Own the end-to-end analytics platform vision: Define the architecture, tooling choices, and standards the data team builds against, combining data engineering, analytics engineering, and BI delivery into a single cohesive strategy
• Work through domain experts, not around them: Partner closely with data engineers, analytics engineers, and analysts to translate that vision into execution, enabling them to go deep in their specialisms rather than doing it all yourself
• Build the analytics foundations the business can rely on: Establish repeatable analysis patterns, metric definitions, testing standards, and the semantic layer, so teams across the company are working from a shared, trusted view of the business
• Transform data access: Design scalable self-serve patterns and explore new approaches to data consumption, from dashboards and embedded analytics to direct tool integrations, so teams can get what they need without raising an ad hoc request
• Elevate analytics from a reporting function to a strategic partner: Introduce causal analytics, advanced analytics, and data science approaches that help the business understand not just what happened, but why, and what to do next
• Bridge data strategy to business outcomes: Act as the primary connection between the data service and the rest of the business, translating business need into data priorities and data strategy into language the C-suite can act on
• Keep the platform ahead of the curve: Continuously evaluate new approaches to analytics delivery, curious about how the field is evolving and willing to introduce those advances in ways that reduce strain on the team
• What success looks like in 12 months:
• Analytics is no longer a bottleneck: teams are making better decisions faster because data is available, trusted, and accessible
• Metric definitions and a semantic layer are in place: one version of the truth, trusted across the business
• Self-serve patterns are real and working: teams can reach the data they need without raising an ad hoc request
• The business is using data in ways that weren't possible before: new approaches to ingestion and consumption have opened up new analytical capabilities
• A clear analytics strategy exists, and the C-suite and data team are aligned around it

• Our processes normally take around 2-3 weeks from first call to offer - please let us know about any adjustments to timelines that may be required
• First call with our Talent Team (30 mins). This is to understand your experience, motivations, and discuss the role in more detail
• Behaviour Interview with our Head of Data (60 mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working
• Technical Interview with the Team (90 mins). You'll meet with potential peers in this session and work through a live technical exercise
• Culture-Add Interview with Stakeholders (45 mins). The final session will be with two cross-functional stakeholders, and will explore how your values align with ours, and is designed to be a genuine two-way conversation, your chance to understand what it's really like to work at tem