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SoSafe is a rapidly growing European human risk management and cybersecurity awareness platform backed by leading VCs (Highland Europe, Global Founders Capital). The company helps organizations address the human factor in cybersecurity through behavioral science and data-driven learning.
In this Senior Analytics Engineer role, you will own the transformation layer and data modeling strategy for the company. You'll design, build, and maintain modular, well-tested data models in dbt that define how data is structured and consumed across SoSafe. Key responsibilities include:
- Owning the dbt transformation layer: design and build modular, well-tested data models that serve as the foundation for analytics and AI use cases.
- Defining and implementing core SaaS business metrics (activation, engagement, retention) as reusable, versioned data assets with consistent definitions across analytics, product, and AI teams.
- Modeling complex SaaS data by integrating product events, CRM (Salesforce), and support data into clean fact and dimension models.
- Building and evolving the semantic layer to create a reliable abstraction over raw data, enabling consistent KPI definitions and supporting downstream consumers including LLM-based analytics agents.
- Collaborating with Data Engineers on upstream data contracts and event schemas to ensure raw data is structured for scalable, reliable analytics.
- Establishing and enforcing best practices in testing, documentation, and data quality as part of the standard development lifecycle.
- Documenting models, metrics, and lineage clearly to enable self-service analytics and reduce ambiguity across teams.
You'll work across a cross-functional environment, turning ambiguous business questions into clear, actionable data models and challenging metric definitions to ensure they reflect real business outcomes.
REQUIREMENTS:
- 5+ years in analytics engineering or data engineering with 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 with cloud data warehouses (BigQuery, Snowflake, or Redshift)
- Experience with metrics/semantic layers (dbt metrics, MetricFlow, Cube, or similar)
- Strong data quality mindset (testing, validation, monitoring)
- Comfortable working with event-based data and cross-functional teams
- Ability to turn ambiguous business questions into clear data models
- Strong business acumen with ability to challenge metric definitions
- Fluent in English
- Work authorization in UK, Ireland, or Portugal
NICE TO HAVE:
- Familiarity with how LLMs consume structured data (semantic layers, metrics registries, YAML-based context) and interest in building data infrastructure for AI agents
- Experience modeling product usage data (event-based or session-based)
