We are seeking a Senior Analytics Engineer — Data Modeling & Visualization to strengthen the Data Team’s semantic modeling, metrics, and dashboarding capabilities.
With access to vast data — thousands of catalogs, millions of products, and billions of entries scraped from e-commerce websites — you’ll help transform complex data into reliable models, clear metrics, and useful dashboards for internal and customer-facing data products.
This role owns the Data Team’s semantic and visualization layer: business-facing data models, metric definitions, dashboards, validation rules, and documentation. You will work closely with the Product Manager, who owns Product-side framing, prioritization, stakeholder alignment, and business acceptance. Your role is to translate Product and business needs into reliable models, clear KPIs, useful dashboards, and maintainable data products.
This is a senior technical role at the intersection of analytics engineering, data modeling, and data visualization. It is not a Product Manager role or a pure Data Engineering role. It is also not focused on ad hoc reporting: requests should be turned into reusable, maintainable data assets whenever they create recurring value.
- Own the semantic and visualization layer of Lengow’s internal and customer-facing data products.
- Define and document KPIs, dimensions, facts, grains, filters, assumptions, and business rules.
- Design reusable analytical models and semantic layers across dashboards and data products.
- Build and improve dashboards in Looker Studio that are clear, useful, performant, and decision-oriented.
- Transform Product and business requirements into modeled datasets, dashboard specifications, validation rules, and documentation.
- Write advanced SQL transformations and contribute to dbt workflows.
- Collaborate with Senior Data Engineers on upstream data quality, data contracts, performance, reliability, and maintainability.
- Challenge unclear metric definitions, incomplete acceptance criteria, misleading visualizations, and one-off requests that should become reusable data products.
- Communicate assumptions, limitations, edge cases, feasibility, risks, and trade-offs clearly to technical and non-technical stakeholders.
Recruitment Process
- Pre-interview: Chat with Alexandre, Head of People (30').
- Team interview: Meet Sebastien (VP Data) and team members (60').
- Final interview: Present a technical case to Sebastien and Olivier (Chief Product and Technology Officer) (60').
Success will be evaluated through the quality, reliability, adoption, and maintainability of internal and customer-facing data products.
Examples of successful outcomes include:
- Critical KPIs have clear definitions, grains, assumptions, and validation rules.
- Dashboards are trusted, documented, actively used, and decision-oriented.
- Dashboard logic is implemented in reusable modeled datasets instead of duplicated across reports.
- Stakeholders understand metric behavior, limitations, and edge cases.
- Product/Data collaboration with the Product Manager is structured and repeatable.
- BI assets meet agreed standards for readability, performance, maintainability, and business usefulness.
Technical environment
Our environment includes SQL, BigQuery, Google Cloud Platform, Cube.js, dbt, Airflow, Looker Studio, Power BI, MariaDB, PostgreSQL, Talend, Apache NiFi, and internal and customer-facing analytics use cases.
