Senior Analytics Engineer
Role details
Job location
Tech stack
Job description
As our first dedicated Analytics Engineer, you will be a foundational part of our data team's expansion. You will join a core team of 3 Data Engineers, and will be one of two new dedicated roles (alongside an AI Automation Engineer) being added to build out our advanced data capabilities. You will be pivotal to GitGuardian's growth and success by formalizing and scaling our analytics engineering function, building the bridge between our core data platform and our business stakeholders.
You will build a reusable analytics platform-trusted data models, well-defined metrics, and clear contracts-that powers internal decisions and customer-facing analytics. Think paved roads, not one-offs: reusable components, great documentation, and governance that makes shipping insights fast and safe.
Your main responsibilities will be to:
- Architect and own data models: Design, build, and maintain end-to-end models in Snowflake as the single source of truth for key domains (Product, Sales, Marketing), with clear contracts and documentation.
- Define metrics & contracts: Establish canonical metrics and data contracts with stakeholders; encode them in versioned, tested transformations to ensure consistency across tools.
- Power In-App analytics: Build and optimize the datasets that power product analytics inside GitGuardian-reliable, performant, and explainable to customers and PMs.
- Champion self-service: Own Metabase and enable self-serve analytics through curated datasets, training, documentation, and guardrails-measured by adoption and autonomy.
- Ensuring data quality & governance: Implement tests, lineage, SLAs, and documentation standards with Data Engineering to make data observable, trusted, and auditable.
- Being a strategic partner: Develop deep domain expertise, challenge assumptions, and use data to shape product and GTM strategy-prioritizing high-ROI outcomes.
- Mentor and elevate: Coach teammates (including interns), codify best practices, and raise the bar for modeling, reviews, and documentation.
- Innovate with AI: Use GenAI to accelerate development, improve docs, and streamline exploration-bringing pragmatic gains into daily workflows.
- Own the roadmap: Set the analytics engineering roadmap and paved paths-what "good" looks like for models, metrics, and self-service at GitGuardian.
Technical Environment
- Core Languages: Python, SQL
- Data Warehouse: Snowflake
- Transformation: in-house framework (dbt-style) with unit tests and documentation
- Orchestration & Deployment: Dagster, Kubernetes, Docker, Terraform
- Ingestion: in-house Python scripts, Fivetran, Airbyte
- Observability: lineage and testing at model and job levels
- Visualization: Metabase
- Sources: PostgreSQL, Elasticsearch, SaaS applications, various APIs
Why is this role unique?
- Build the analytics backbone: Establish GitGuardian's analytics engineering function and ship the paved roads that every team uses.
- Impact you can measure: Your models and metrics power both internal decision-making and in-app analytics used by our customers.
- Autonomy with executive backing: Direct access to decision-makers and mandate to set standards, from modeling to governance.
- Modern data craft: Work with Snowflake, dbt-like transformations, Dagster-and shape how we apply GenAI across the analytics lifecycle.
Requirements
Do you have experience in Terraform?, * 5-7+ years in data teams with a strong track record in Analytics Engineering (or equivalent) shipping production-grade models and metrics.
- Expertise with SQL and production experience with dbt (Core/Cloud). You've designed, deployed, and maintained complex models with tests, docs, and version control. (dbt experience required even if our runtime is in-house.)
- Product mindset for analytics: you translate ambiguous questions into clear metrics, robust models, and artifacts people actually use.
- Self-service champion: you measure success by adoption and autonomy-fewer ad hoc requests, more empowered teams.
- Pragmatic and autonomous communicator: comfortable advising leadership, setting standards, and driving alignment across Product, GTM, and Engineering.
- Proficiency in Python for data manipulation, scripting, and tooling around the transformation layer.
- Familiarity with GenAI to accelerate development, exploration, and documentation (practically, not hype).
- Fluency in English (verbal and written).
The following skills would strengthen your application but aren't required:
- High-growth B2B SaaS experience.
- Experience with our stack (Dagster, Fivetran/Airbyte, Metabase).
- Prior experience scaling an analytics function and defining paved paths (models, metrics, self-service).
- Exposure to data ingestion/engineering and data contracts.
Benefits & conditions
- Package that includes stock-options
- Lunch voucher (Swile)
- Non-charged health insurance for children (Sidecare / Generali)
- Up to €300 to improve your home office set-up
- Yearly holiday allowance
- Referral bonus of 4000€ for any new Guardian we might hire thanks to you
- Team building: monthly budget dedicated to each employee that you can spend as you wish, with colleagues (latest examples to date: Michelin star restaurant, karaoke, stand-up show, kitesurfing week-end, ...)
And also...
- Remote policy: hybrid (3 days/week at the office)
- Opportunities for career development in the long term