Uses Hex and Lovable for rapid data UIs and lists AI/LLM experience as a nice-to-have, so there's some overlap with AI-assisted/vibe coding and rapid prototyping with AI tools.
About the Role
Lovable is hiring an Analytics Engineer to build and own the data foundations that power GTM teams — converting raw data into reliable warehouse models, metrics, dashboards, and automated workflows to support Sales and Customer Success.
Job Description
Role
As an Analytics Engineer at Lovable you will own the end-to-end data foundation for GTM teams: build and maintain warehouse models, design metrics and dashboards, enable CRM and usage-derived automation, and turn ambiguous business questions into structured, operational data products.
Key Responsibilities
- Partner with Sales and Customer Success to translate business needs into data models, analyses, and actionable insights.
- Build and maintain data warehouse models as the single source of truth for product usage and GTM metrics.
- Model CRM, usage, and billing data to support CRM enrichment, lead creation, and GTM tooling.
- Design metrics, dashboards, and data UIs in Hex and Lovable for leadership and day-to-day use.
- Collaborate with Product and Engineering on event instrumentation and schema design.
- Improve documentation, observability, governance, and data quality testing for pipelines and ingestion workflows.
- Contribute to forecasting models, KPI definitions, and experimentation frameworks.
Requirements
- Strong SQL skills and analytical data modeling experience.
- Experience with ELT/ETL workflows and cloud data warehouses.
- Comfort using Python for automation and light data engineering tasks.
- Experience building dashboards, BI tools, and enabling self-serve analytics.
- Clear communication and collaboration skills; able to work in ambiguity and partner cross-functionally.
- Product-minded approach to analytics and ownership of data as a product.
Nice to have
- Experience with dbt or SQLMesh.
- Experience with AI/LLM products, instrumentation, experimentation, or early-stage startups.
Hiring process (summary)
- Short form and an exploratory call.
- Interview round(s) to discuss experience.
- A 1–2 day workshop (remote or onsite) to evaluate fit and working style.