[Hiring] Senior Analytics Engineer @SoSafe

Role Description

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.

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.

Qualifications

• 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

Requirements

• 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).

Benefits

• 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