About the role:

At Dashlane, we believe that data is the engine of our growth and the voice of our customer. We are looking for someone to join our Data & Analytics team as a Lead Analytics Engineer. You will be a strategic partner to the business, shaping the future of how Dashlane makes decisions.

You will be a strategic leader on the team, responsible for translating complex business challenges into scalable and trusted data solutions. Your work will directly shape how Dashlane measures success and makes critical decisions, from executive-level financial reporting to product-led growth experiments. You will act as a force multiplier by elevating the work of the entire team through mentorship, establishing best practices, and building scalable data products and self-service tools that empower the entire organisation. If you are passionate about treating data as a product and want to have a measurable impact on a thriving B2B/B2C SaaS business, this role is for you.

Location-Specific Information:

You will be based in Paris, with English as your working language. We offer a hybrid work arrangement, with Tuesday as the company day where we collaborate in the office and enjoy a company-sponsored meal, a department day for team bonding, and a third day of your choice.

At Dashlane, you will:

• Lead & own Data models: Design, build, and own end-to-end data models within our dbt-powered UDM that serve as the single source of truth for key business domains like Finance, Product, and Go-to-Market.

• Act as a strategic partner: Go beyond simple collaboration to develop deep domain expertise in our key business areas. Use this context to form a strong, data-informed point of view, challenge assumptions, and directly influence product and business strategy.

• Champion self-service: Be a primary driver of our self-service analytics culture. You will not only build trusted datasets but also help select, implement, and provide stakeholder training on modern BI and semantic layer tools.

• Mentor and elevate the team: As a senior member, you will mentor other analytics engineers, establish and advocate for best practices, and elevate the quality of our work through thoughtful code reviews and pairing sessions.

• Drive Data governance & quality: Work with your Data Engineering counterparts to implement and enforce data quality tests, documentation standards, and governance policies that ensure our data is always accurate, reliable, and trusted.

• Leverage AI to accelerate impact: Proactively use modern GenAI tooling to improve your development velocity, enhance documentation, and accelerate the path from raw data to business insight.

Our Tech Stack:

• Data Modeling & Transformation: dbt

• Programming Languages: SQL, Python

• Data Platform: AWS (Redshift, S3, Lambda, Kinesis, Glue)

• BI & Visualisation: Tableau (and soon, a new modern BI/Semantic Layer tool)

• Orchestration: Airflow

• CI/CD & Version Control: GitLab

Requirements:

• You have 7+ years of experience in an analytics engineering position or equivalent role.

• You are an expert in SQL and dbt, with deep experience designing, deploying, and maintaining complex data models in a production environment.

• You have a strong analytics mindset and clear business acumen, with demonstrated experience translating ambiguous business problems into robust, scalable data solutions that have driven measurable business impact.

• You have deep, practical experience working in and helping to foster a mature self-service analytics environment. You measure your success by the autonomy you create for others.

• You are a highly autonomous, proactive communicator with exceptional stakeholder management skills. You are comfortable advising senior leadership and driving alignment across teams.

• You have a growth mindset and significant experience in a B2B SaaS environment. You can speak the language of ARR, NRR, churn, and product-led growth.

• You have practical experience using GenAI tools across the entire analytics lifecycle, not only to accelerate your development workflow but also to aid in data exploration, hypothesis generation, and summarising analytical findings.

• Fluent in English: verbal and written.