Position Overview

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.

What We’re Looking For

The ideal candidate:

• Enjoys building reliable data systems and solving complex data problems

• Has strong technical data engineering skills

• Understands how revenue teams use data for reporting and decision-making

• Can translate business context into scalable data models

• Is comfortable working across multiple systems and client environments

• Is comfortable working directly with clients as needed

• Thrives in collaborative, fast-paced environments

Key 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

Required Qualifications

• 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

Preferred Experience

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.)

Learn more about what it's like to work at Lean Layer here.

For Brazilian Residents: This role is open to candidates based in Brazil only and is a contract-to-hire opportunity. You’ll start on an hourly contract, with up to 40 hours of work per week. The salary listed reflects the full-time compensation for the role, and we’ll set your hourly rate based on your full-time expectations. If you have any questions about this setup, please don’t hesitate to reach out.