Revenue Technology - Data Strategy & Operations Lead

Mercury Mercury · Fintech · Remote · Sales Operations

Mercury is seeking a Data Strategy & Operations Lead to own the data foundations powering revenue execution. This role involves defining, structuring, and ensuring the reliability of revenue data from various platforms, designing core data models, and partnering with Data Engineering and Data Science to ensure data is interpretable and scalable. The goal is to make revenue data trustworthy and actionable for business growth.

What you'd actually do

  1. Own the definition, structure, and reliability of data originating from revenue platforms (e.g., Salesforce, GTM tools, automation systems)
  2. Serve as the primary decision owner for GTM-sourced tables and views used for revenue execution, forecasting inputs, lifecycle tracking, and signal-based workflows
  3. Design and evolve core GTM data models across Salesforce, ETL, and analytics layers
  4. Partner with Data Engineering to align GTM schemas with enterprise data models and define clear data contracts between source systems and downstream consumers
  5. Partner with Data Science / Analytics to ensure revenue data is interpretable, statistically sound, and reflects how the business actually operates

Skills

Required

  • 7+ years of experience in data engineering or data systems roles within SaaS or technology companies
  • deep experience designing and operating production data pipelines
  • highly proficient in SQL
  • experienced in data modeling
  • hands-on experience with modern data stacks (e.g., Snowflake, BigQuery, Redshift)
  • experience with ETL / ELT tooling (e.g., dbt, Airflow, Census, or similar)
  • Understand Salesforce data models and common GTM system architectures
  • able to translate business concepts into durable, well-structured data models
  • Communicate clearly with both technical and non-technical partners

Nice to have

  • Experience supporting revenue, sales, or customer lifecycle data
  • Familiarity with event-based data platforms (e.g., Data Cloud or equivalents)
  • Experience working alongside platform engineering and security teams
  • Exposure to data governance, access controls, and compliance considerations
  • Experience mentoring or guiding other data practitioners

What the JD emphasized

  • data system that people can actually trust
  • clear, reliable intelligence
  • brittle pipelines
  • constant rework
  • reliable, interpretable, scalable, and usable
  • act on what they see with confidence
  • data foundations
  • revenue execution
  • reliable
  • interpretable
  • scalable
  • usable
  • confidence
  • data integrity
  • data quality
  • freshness
  • consistency
  • documentation standards
  • pipeline reliability
  • performance
  • scalability
  • fragile or redundant transformations
  • automate manual or error-prone data workflows
  • reduce operational overhead