SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Lovable is a platform that enables anyone to build software with any language, used by over 2 million people across 200+ countries. The company is at the forefront of a foundational shift in software creation and is building a generation-defining product.
We're seeking an Analytics Engineer to own the data foundations that fuel the GTM (Go-To-Market) teams. You will build reliable data models, shape metrics, power automation, and turn raw data into insights and data products that drive Sales and Customer Success teams.
Key responsibilities include:
- Partnering with Sales and CS on data and reporting needs, translating ambiguous business questions into structured data models and actionable insights
- Building and maintaining data warehouse models that serve as the source of truth for product usage and GTM metrics
- Modeling CRM data, usage data, and billing data to power Sales and CS automation, CRM enrichment, lead definition, and GTM tooling
- Designing metrics, dashboards, and data UIs in Hex and Lovable for GTM leadership and teams
- Partnering with Product and Engineering on event instrumentation and schema design
- Improving documentation, observability, governance, and data best practices
- Contributing to forecasting models, KPI definitions, and experimentation frameworks
- Owning and improving data pipelines, ingestion workflows, and data quality testing
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, and experience with dashboards and BI tools. Nice-to-have skills include experience with AI/LLM products, instrumentation, experimentation, or early-stage startups.
You thrive by building clean, reliable, reusable data models, partnering closely with cross-functional GTM teams, preferring simplicity over complexity, communicating clearly with technical and non-technical teams, taking ownership and moving quickly, and helping define and scale Lovable's data foundations.
