Role overview

Senior Analytics Engineer

Requirements and responsibilities

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Here's how you'll make a difference:

  • Own the transformation layer in dbt- design, build, and maintain modular, well-tested data models that define how data is structured and consumed across the company.
  • Define and implement core business metrics (e.g. activation, engagement, retention) as reusable, versioned data assets- ensuring consistent definitions across analytics, product, and AI use cases.
  • Model complex SaaS data by integrating product events, CRM (Salesforce), and support data into clean, well-defined fact and dimension models.
  • Build and evolve our semantic layer- creating a reliable abstraction over our data that enables consistent KPI definitions and supports downstream consumers, including LLM-based analytics agents.
  • Collaborate with Data Engineers on upstream data contracts and event schemas- ensuring raw data is structured in a way that supports scalable, reliable analytics.
  • Establish and enforce best practices in testing, documentation, and data quality- making these part of the standard development lifecycle.
  • Document models, metrics, and lineage clearly- enabling self-service and reducing ambiguity across teams.

What you bring:

  • 5+ years in analytics engineering or data engineering with a strong focus on data modeling
  • Strong proficiency in dbt and SQL- building modular, well-tested models
  • Solid understanding of dimensional modeling and metric design
  • Experience working with cloud data warehouses (BigQuery, Snowflake, or Redshift)
  • Experience with metrics / semantic layers (e.g. dbt metrics, MetricFlow, Cube)
  • Strong data quality mindset (testing, validation, monitoring)
  • Comfortable working with event-based data and cross-functional teams
  • Able to turn ambiguous business questions into clear data models
  • Strong business acumen with the ability to challenge metric definitions and ensure they reflect real business outcomes
  • Fluent in English.

Nice to have:

  • Familiarity with how LLMs consume structured data- e.g. semantic layers, metrics registries, YAML-based context- and an interest in building data infrastructure that serves AI agents, not just BI tools.
  • Experience modeling product usage data (event-based or session-based).

What we offer*

  • Work/Life balance: Flexible hours, 33 vacation days
  • Wellbeing and financial support: Access to Open Up, corporate discounts
  • Connection & community: Virtual events, collaborative team activities, and opportunities for local meet-ups
  • And the list goes on: Tech equipment, referral bonuses, dog-friendly HQ
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Browse stack
FocusSenior Analytics EngineerRole area
Seniority signalSeniorCandidate level
StackLLM, Salesforce, SnowflakePrimary skills
Location38 accepted countriesEligibility

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