Senior Analytics Engineer - Data Modeling & Visualization

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.

We’re looking for a senior data professional who combines technical rigor, strong data modeling skills, visualization expertise, and business understanding. You should be able to understand a business question, challenge vague definitions, design the right analytical model, and deliver a dashboard or data product that can be trusted over time. Here’s what you bring to the table: • Experience: 5+ years in analytics engineering, data visualization engineering, BI engineering, data engineering, or a similar data role. • Data modeling: strong understanding of facts, dimensions, grains, aggregations, metric definitions, semantic consistency, and business rules. • Data visualization: proven ability to design dashboards that are not only visually clear, but useful for real decisions. • Engineering fluency: advanced SQL, strong analytical rigor, and good understanding of data quality, lineage, testing, and transformation workflows. • Collaboration: ability to work with Product, business stakeholders, and Senior Data Engineers without becoming a substitute Product Manager or a support desk. • Mindset: autonomous, structured, constructive, and able to push back when definitions, requirements, or implementation choices are unclear or unsafe. • Tools & tech: experience with SQL, BigQuery, Google Cloud Platform, dbt, Airflow, Cube.js, Looker Studio, Power BI, MariaDB, and PostgreSQL. Familiarity with ETL/ELT tools such as Talend or Apache NiFi is a plus in our current context. We value your ideas and welcome recommendations on tools, modeling practices, visualization standards, and data product quality. Bonus points if you: • Know and are passionate about web, e-commerce, marketplaces, retail, SaaS, or product analytics. • Have worked on customer-facing dashboards or data products. • Have experience with semantic layers, metrics layers, or governed KPI frameworks. • Have experience with data contracts or data quality frameworks. • Have strong UX instincts for data products and dashboards. What success looks like 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. • Sta

✨ Joining Lengow is also an opportunity to benefit from many advantages : • Ticket restaurant 8 euros by day • Malakoff Humanis Private insurance & Prevoyance. • 3 Remote days per week • Flexible hours • Bike mileage allowances or 50% of transportation tickets. • Remote allowances • Professional events (Devoxx, Meetup ...) and regular internal cohesion. • Weekly Happy Break on Thursday Evening at the office with food and beverage • Syntec forfait jours with RTT - 218 annual working days, ie minimum 9 days off on top of 5 weeks legal paid leave • Choose your laptop OS. You can work on MacOS, Windows or Linux.

Since 2009, Lengow has been the indispensable e-commerce platform for multi-channel expansion in the European market: marketplaces, price comparison websites, affiliate marketing, display ad retargeting, social media, etc. Our Data team Join a dynamic Tech, Data & Product team of 50 professionals with diverse expertise. You’ll report to the Head of Data and work closely with our Tech, Product, and business teams. The Data Team is a key driver of innovation at Lengow. From enabling access to insights for employees to creating custom data products for clients, our mission is to transform raw data into business impact. We’re expanding our scope and shaping the future of data at Lengow. What the Data team does • Empower business decisions with relevant insights and accessible data tools. • Guide product development through data-driven features, metrics, and insights. • Manage and optimize custom data products for clients. • Enhance our data stack and tools for scalability, reliability, and performance. • Cultivate a data-first culture by translating business needs into actionable insights. Your mission 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. Your key responsibilities: • 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'). We’re looking for a senior data professional who combines technical rigor, strong data modeling skills, visualization expertise, and business understanding. You should be able to understand a business question, challenge vague definitions, design the right analytical model, and deliver a dashboard or data product that can be trusted over time. Here’s what you bring to the table: • Experience: 5+ years in analytics engineering, data visualization engineering, BI engineering, data engineering, or a similar data role. • Data modeling: strong understanding of facts, dimensions, grains, aggregations, metric definitions, semantic consistency, and business rules. • Data visualization: proven ability to design dashboards that are not only visually clear, but useful for real decisions. • Engineering fluency: advanced SQL, strong analytical rigor, and good understanding of data quality, lineage, testing, and transformation workflows. • Collaboration: ability to work with Product, business stakeholders, and Senior Data Engineers without becoming a substitute Product Manager or a support desk. • Mindset: autonomous, structured, constructive, and able to push back when definitions, requirements, or implementation choices are unclear or unsafe. • Tools & tech: experience with SQL, BigQuery, Google Cloud Platform, dbt, Airflow, Cube.js, Looker Studio, Power BI, MariaDB, and PostgreSQL. Familiarity with ETL/ELT tools such as Talend or Apache NiFi is a plus in our current context. We value your ideas and welcome recommendations on tools, modeling practices, visualization standards, and data product quality. Bonus points if you: • Know and are passionate about web, e-commerce, marketplaces, retail, SaaS, or product analytics. • Have worked on customer-facing dashboards or data products. • Have experience with semantic layers, metrics layers, or governed KPI frameworks. • Have experience with data contracts or data quality frameworks. • Have strong UX instincts for data products and dashboards. What success looks like 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. • Dash