Analytics Engineer (AI)

TL;DR

Analytics Engineer (AI): Building reliable data foundations, warehouse models, metrics, and operational data products for Sales and Customer Success with an accent on GTM analytics, automation, and self-serve reporting. Focus on designing scalable data models, improving pipeline quality, partnering on instrumentation, and turning ambiguous business questions into actionable insights.

Location: Hybrid work in Stockholm, Boston, or New York

Company

Lovable is a software product company enabling individuals and teams to build and launch applications using natural language and any programming language.

What you will do

• Partner with Sales and Customer Success to translate business questions into data models, analysis, reporting, and operational data products.
• Build and maintain warehouse models for product usage and GTM metrics across CRM, usage, and billing data.
• Power sales and customer success automation, CRM enrichment, lead creation, and GTM tooling.
• Design metrics, dashboards, and data interfaces in Hex and Lovable for self-serve analytics.
• Partner with Product and Engineering on event instrumentation and schema design.
• Improve pipelines, ingestion workflows, documentation, observability, governance, and data quality testing.

Requirements

• Strong SQL and analytical data modeling skills, ideally with dbt or SQLMesh.
• Experience with ELT/ETL workflows and cloud data warehouses such as Snowflake, BigQuery, Redshift, or Databricks.
• Python experience for automation and light data engineering.
• Experience with dashboards, BI tools, and self-serve analytics.
• Clear communication, proactive collaboration, and comfort working with ambiguity across technical and non-technical teams.
• English is the company language and is required for the application and day-to-day communication.

Nice to have

• Experience with AI or LLM products.
• Experience with instrumentation, experimentation, or early-stage startups.
• Experience partnering with cross-functional GTM teams and treating analytics as a product.

Culture & Benefits

• Small, talent-dense team with a focus on ownership, high velocity, and low-ego collaboration.
• Opportunity to define and scale data foundations and analytics culture.
• Emphasis on simplicity, reusable models, fast iteration, and practical business impact.
• Application process begins with a short form and an exploratory call, followed by interviews.