Senior Analytics Engineer

About the role

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:

Lisbon, Portugal. Candidates must have work authorization and based in Portugal. Office access available 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/Life balance: Flexible hours, 33 vacation days