Staff Analytics Engineer, Lime for Business

Role Overview

As the founding Staff Analytics Engineer for Lime for Business, you will define and lead the analytics architecture for a new B2B product vertical. You’ll own the design of the business data warehouse, semantic layer, and metric foundations that enable reporting, experimentation, financial tracking, and operational insights.

This is a highly cross-functional technical leadership role across Product, Engineering, Data Science, Finance, Accounting, and Operations.

Key Responsibilities

• Analytics architecture: Own the long-term technical vision for the data warehouse and semantic layer.
• Business-to-data modeling: Translate complex concepts (e.g., employer subsidies, commuter utilization, revenue recognition, trip profitability) into scalable, well-tested data models.
• Modeling standards: Establish standards using dbt for consistency, reusability, and performance.
• Cross-functional partnerships: Work with Product, Engineering, Data Science, Operations, Finance, and Accounting to deliver trusted data products.
• Metrics and experimentation: Serve as a thought partner for metric definitions, experimentation design, and KPI tracking.
• Analytics engineering excellence:
  • Build robust ETL/ELT pipelines using Snowflake, Airflow, and dbt.
  • Define best practices for SQL style, modular modeling, and CI/CD workflows.
  • Implement automated data testing, documentation, observability, and auditability.
• Data governance and quality:
  • Build automated data validation and monitoring.
  • Support month-end close reporting and external audit readiness with Finance and Accounting.
  • Ensure integrity and reliability of business-critical metrics.

Requirements

• 7+ years experience in analytics engineering, data warehousing, or data architecture.
• Production expertise with Snowflake, dbt, and Airflow.
• Proven ability designing scalable data models and semantic layers for business reporting.
• Track record leading large technical domains and setting architectural direction.
• Strong SQL skills and experience implementing automated testing/validation (posting cuts off, but indicates automation expectations).

Nice-to-haves (implied)

• Experience supporting financial close cycles and audit processes via data pipelines.
• Demonstrated technical mentorship and emphasis on documentation, craftsmanship, and data quality.