Senior Analytics Engineer
Summary
Senior Analytics Engineer at SoSafe, a cybersecurity awareness training company in London, owning the dbt transformation layer: building modular data models, defining core SaaS metrics (activation, engagement, retention), and evolving a semantic layer that powers analytics and AI agents using product events, Salesforce CRM, and support data.
In this role you will own the transformation layer with dbt, building modular data models and a reliable semantic layer to power analytics and AI agents. You will define and maintain core business metrics, model SaaS data across product, CRM, and support data, and collaborate with cross-functional teams to ensure data quality and self-service. You’ll shape how data is consumed across analytics, product, and AI use cases, enabling scalable, trusted insights. This is a mission-driven opportunity to advance data infrastructure that supports security awareness scale-ups in Europe.
Pay / Benefits- flexible hours
- 33 vacation days
- wellbeing and financial support
- virtual events
- local meet-ups
- tech equipment
- Design, build, and maintain modular dbt data models that structure and consume data across the company
- Define and implement core metrics (activation, engagement, retention) as reusable data assets
- Model complex SaaS data by integrating product events, Salesforce CRM, and support data into dimensional models
- Develop and evolve the semantic layer to support KPI definitions and downstream consumers, including AI analytics agents
- Collaborate with Data Engineers on upstream data contracts and event schemas to ensure scalable analytics
- Establish and enforce data quality, testing, and documentation as part of the development lifecycle
- Document models, metrics, and lineage to enable self-service and reduce ambiguity across teams
- 5+ years in analytics engineering or data engineering with a focus on data modeling
- Strong proficiency in dbt and SQL
- Solid understanding of dimensional modeling and metric design
- Experience 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 with event-based data and cross-functional collaboration
- Ability to turn ambiguous business questions into clear data models
- Strong business acumen and ability to challenge metric definitions
- Fluent in English
- cross-functional collaboration
- problem-solving
- communication
- dbt
- SQL
- dimensional modeling