RevOps Analytics Engineer

Lean Layer•New York, NY

About The Position

Lean Layer is the #1 Rated RevOps Agency on G2, and we’re doubling our consulting team over the next year. Our reputation is built on excellent results, which means we need to keep hiring excellent people. We are looking for a RevOps Analytics Engineer with deep Revenue Operations expertise to own and maintain the data infrastructure that powers revenue analytics and reporting across our client environments. This role focuses on data engineering and warehouse management, ensuring reliable pipelines, scalable data models, and high-quality revenue data. The RevOps Analytics Engineer will work closely with RevOps consultants who define CRM and business requirements, and with data analysts who build dashboards and reporting. You may be a fit for the RevOps Analytics Engineer role if you are strong in SQL, data modeling, and warehouse architecture, and can understand the business context of revenue operations in order to build reliable and scalable data systems.

Requirements

3–5 years of experience in data engineering or analytics engineering
Strong SQL skills
Experience working with data warehouses (BigQuery, Snowflake, Redshift, etc.)
Experience working with Salesforce or HubSpot as a data source
Experience building and maintaining ETL / ELT pipelines
Experience designing analytics-ready data models
Familiarity with API-based integrations and data syncing
Python for data pipelines or automation
Reverse ETL or operational data workflows
dbt or similar transformation tools
Looker or similar BI platforms
Experience with GitHub

Nice To Haves

Experience working with revenue or business systems and terminology such as:
Marketing Automation Platforms (MAP) like HubSpot
Marketing analytics platforms
SaaS revenue metrics (ARR, ACV, TCV, MRR, etc.)
SaaS terminology (MQL, SQL, SQO, Deal/Opportunity, Lead/Contact, etc.)

Responsibilities

Data Warehouse Ownership:
Design and maintain datasets and table structures
Manage warehouse performance, partitioning, clustering, and cost optimization
Maintain access controls and permissions
Structure warehouse schemas to support revenue analytics and reporting
Data Pipelines & Integrations:
Build and maintain ETL / ELT pipelines from revenue systems into the warehouse
Integrate data from systems such as HubSpot, Salesforce, marketing and sales analytics platforms, sales engagement platforms, billing systems, and product analytics tools
Monitor pipeline health and resolve failures
Manage schema changes from upstream systems
Ensure reliable and timely data synchronization
Manage GitHub repositories
Data Modeling for Revenue Analytics:
Design and maintain analytics-ready data models
Build models for accounts, contacts, opportunities, and pipeline data
BI & Analytics Support:
Maintain tables and models used by BI tools such as Looker
Optimize queries and support derived tables used in reporting
Ensure consistent metric definitions across reporting layers
Dashboard creation for data validation
Data Quality & Reliability:
Implement data validation and testing
Monitor pipeline health and data freshness
Identify and resolve data inconsistencies
Maintain documentation for warehouse models and data definitions