Senior Analytics Engineer - name

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 (remote). 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

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/Lif