SoSafe
Senior Analytics Engineer
Remote Senior Analytics Engineer role with clear candidate location fit.
PostedJun 22, 2026
Eligible countries38 accepted countries
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Role overview
Senior Analytics Engineer
Requirements and responsibilities
Readable role content extracted into sections for faster review.
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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