Senior Analytics Engineer

🕒 April 21

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SoSafe

WebsiteLinkedInAll Job Openings

201 - 500 employees

🔒 Cybersecurity

📚 Education

☁️ SaaS

💰 $73M Series B on 2022-01

Cybersecurity • Education • SaaS

SoSafe is a leading human risk management platform that specializes in strengthening security culture and behavior within organizations. The company offers a variety of products and solutions including personalized micro-learning, smart phishing simulations, and an AI-powered conversational chatbot named Sofie. SoSafe's platform leverages behavioral science to enhance security instincts among employees, making secure behavior second nature. They provide a comprehensive human risk dashboard and continuously monitor and mitigate human risk in real time with their Human Risk OS platform. SoSafe aims to empower organizations to become more proactive in their defense against cyberthreats and to integrate secure behavior into their organizational DNA. They emphasize the importance of a security culture in the compliance process and offer various resources and events to support security leaders. SoSafe is recognized for its innovative and privacy-focused solutions that keep organizations ahead of cyberthreats, enabling them to achieve compliance and a robust security posture.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ 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