Analytics Engineer: Build Data Foundations for GTM

Lovable

Boston, New York (MA, NY)

Hybrid

USD 120,000 - 180,000

Full time

14 days+

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Job summary

Lovable is seeking an Analytics Engineer to own data foundations that power GTM teams. You will build reliable models, shape metrics, and automate workflows, turning raw data into insights and data products for Sales and Customer Success.

You will collaborate with Sales, CS, Product, and Engineering to instrument events, define schemas, and create dashboards in Hex and Lovable to operationalize data for leadership.

Qualifications

  • Strong SQL and data modeling for analytics
  • Experience with ELT/ETL and cloud data warehouses
  • Proficiency in Python for automation
  • Experience building dashboards and self-serve analytics
  • Clear communication across teams, comfortable with ambiguity
  • Nice to have: AI/LLM products and instrumentation

Responsibilities

  • Partner with Sales and CS on data needs and translate questions into data models and insights
  • Build and maintain data warehouse models serving product usage and GTM metrics
  • Model CRM, usage, and billing data to power automation and CRM enrichment
  • Design metrics, dashboards, and data UIs for GTM leaders
  • Collaborate with Product and Engineering on instrumentation and schemas
  • Improve data governance, observability, and quality testing
  • Support forecasting models, KPI definitions, and experimentation frameworks
  • Own data pipelines and ingestion workflows

Skills

SQL
Data Modeling
ELT/ETL
Python
BI Dashboards
Communication
AI/LLM

Tools

Snowflake
BigQuery
Redshift
Databricks

Job description

Lovable is seeking an Analytics Engineer to own data foundations that power GTM teams. You will build reliable models, shape metrics, and automate workflows, turning raw data into insights and data products for Sales and Customer Success.

You will collaborate with Sales, CS, Product, and Engineering to instrument events, define schemas, and create dashboards in Hex and Lovable to operationalize data for leadership.

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