Job description

TL;DR - We’re seeking an Analytics Engineer to own the data foundations that fuel the GTM teams: building reliable models, shaping metrics, powering automation, and turning raw data into the insights and data products that drive Sales and Customer Success. WHY LOVABLE? Lovable is the software creation platform that gives people the power to act on the problems closest to them. For decades, turning an idea into software required so much capital, technical fluency, and time that many ideas never came to life. Lovable is the counterargument: a platform for all people with ideas, ambition, and problems worth solving. From solopreneurs to small business owners to teams at companies like Adidas and Zendesk, people have built over 60 million projects on Lovable since its launch in November 2024. And we’re just getting started. We’re building a generational company from Stockholm, with growing teams in London, Boston, New York, and San Francisco. Our team is small, talent-dense, and moving quickly, with a culture rooted in extreme ownership, high velocity, and low-ego collaboration. We look for people who care deeply, ship fast, and are eager to make a dent in the world. Lovable is one of TIME’s 100 Most Influential Companies and has been recognized on the Forbes AI 50 and CNBC Disruptor 50, reflecting our momentum as one of Europe’s fastest-growing AI companies and one of the most ambitious places to build in this next era of software. WHAT WE'RE LOOKING FOR We’re looking for an Analytics Engineer with a background in Data Engineering or Analytics Engineering who operates as a true full-stack analyst, owning everything from raw data to insights to operationalization. You bring: - Strong SQL and analytical data modeling skills (ideally dbt or SQLMesh). - Experience with ELT/ETL workflows and cloud warehouses (Snowflake, BigQuery, Redshift, Databricks). - Comfort with Python for automation and light data engineering. - Experience with dashboards, BI tools, and self-serve analytics. - Clear communication, collaboration, and comfort working in ambiguity. - Nice to have: experience with AI/LLM products, instrumentation, experimentation, or early-stage startups. WHAT YOU'LL DO - Partner with Sales and CS on data and reporting needs. Translate ambiguous business questions into structured data models, analysis and actionable insights, and data-fueled products. - Build and maintain data warehouse models that serve as the source of truth for product usage and GTM metrics. - Model CRM data, usage data, and billing data to power Sales and CS automation, CRM enrichment, lead definition and creation, and GTM tooling. - Design metrics, dashboards, and data UIs in Hex and Lovable for GTM leadership and teams to access and operationalize data. - Partner with Product and Engineering on event instrumentation and schema design. - Improve documentation, observability, governance, and data best practices. - Contribute to forecasting models, KPI definitions, and experimentation frameworks. - Own and improve data pipelines, ingestion workflows, and data quality testing. YOU’LL THRIVE HERE IF YOU - Enjoy building clean, reliable, reusable data models. - Have experience partnering closely with cross-functional GTM teams to solve real problems with data. - Prefer simplicity over complexity in data and tooling design. - Communicate clearly and proactively with technical and non-technical teams. - Take ownership, move quickly, and iterate often. - Want to help define and scale Lovable’s data foundations and culture. - Care about the rep experience and view analytics as a product. HOW WE HIRE 1. Fill in a short form then jump on an initial exploratory call. 2. Discuss your experience in more depth during a round of interviews with us. ABOUT YOUR APPLICATION - Please submit your application in English. It’s our company language so you’ll be speaking lots of it if you join. - We treat all candidates equally - if you’re interested please apply through our careers portal.

Requirements

Must-haves

WHY LOVABLE?
We’re looking for an Analytics Engineer with a background in Data Engineering or Analytics Engineering who operates as a true full-stack analyst, owning everything from raw data to insights to operationalization.
Strong SQL and analytical data modeling skills (ideally dbt or SQLMesh).
Experience with ELT/ETL workflows and cloud warehouses (Snowflake, BigQuery, Redshift, Databricks).
Comfort with Python for automation and light data engineering.
Experience with dashboards, BI tools, and self-serve analytics.
Clear communication, collaboration, and comfort working in ambiguity.
Partner with Sales and CS on data and reporting needs. Translate ambiguous business questions into structured data models, analysis and actionable insights, and data-fueled products.
Build and maintain data warehouse models that serve as the source of truth for product usage and GTM metrics.
Model CRM data, usage data, and billing data to power Sales and CS automation, CRM enrichment, lead definition and creation, and GTM tooling.
Design metrics, dashboards, and data UIs in Hex and Lovable for GTM leadership and teams to access and operationalize data.
Partner with Product and Engineering on event instrumentation and schema design.
Improve documentation, observability, governance, and data best practices.
Contribute to forecasting models, KPI definitions, and experimentation frameworks.
Own and improve data pipelines, ingestion workflows, and data quality testing.
Enjoy building clean, reliable, reusable data models.
Have experience partnering closely with cross-functional GTM teams to solve real problems with data.
Prefer simplicity over complexity in data and tooling design.
Communicate clearly and proactively with technical and non-technical teams.
Take ownership, move quickly, and iterate often.
Want to help define and scale Lovable’s data foundations and culture.
Care about the rep experience and view analytics as a product.
Fill in a short form then jump on an initial exploratory call.

Nice-to-haves

Nice to have: experience with AI/LLM products, instrumentation, experimentation, or early-stage startups.

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