Analytics Engineer

SoSafe has the ambition to become the leading human risk management provider in Europe. Our award-winning awareness platform triggers behavioural change by providing effective and engaging training and simulations on cybersecurity and data protection. Cybercrime is costing the world >$10 trillion annually and growing by 15% p.a. - we invite you to be part of the solution!

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).