Senior Analytics Engineer

Job 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. Cybercrime is costing the world >$10 trillion annually and growing by 15% p.a. - we invite you to be part of the solution!" Location: UK, Ireland, or Portugal. Candidates must have work authorization in one of these countries. Office access available in London, Dublin, and Lisbon. 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…