Revenue Technology – Data Strategy & Operations Lead

Job Duties

• Data Ownership: Own the definition, structure, and reliability of data originating from revenue platforms such as Salesforce and various GTM (Go-To-Market) automation tools.

• Model Design: Design and evolve core GTM data models across Salesforce, ETL, and analytics layers to ensure they reflect actual business operations.

• Cross-Functional Partnership: Collaborate with Data Engineering to align GTM schemas with enterprise models and define strict data contracts between source systems and downstream consumers.

• Decision Authority: Serve as the primary owner for GTM-sourced tables and views used for critical forecasting, lifecycle tracking, and revenue execution.

• Quality Assurance: Define and uphold rigorous standards for data quality, freshness, consistency, and documentation across all revenue platforms.

• Reliability Monitoring: Monitor pipeline performance and scalability; proactively identify and fix fragile or redundant transformations.

• Workflow Automation: Identify opportunities to automate manual or error-prone data workflows to reduce operational overhead.

• Strategic Advising: Act as a data thought partner to Revenue Operations, Analytics, and Security teams, advising on the feasibility and sequencing of data-heavy initiatives.

• Governance & Integrity: Own clarity around data ownership boundaries and establish escalation paths for changes that impact revenue data integrity.

Requirements

• Technical Depth: 7+ years of experience in data engineering or data systems roles within the SaaS or technology sectors.

• SQL Mastery: High proficiency in SQL and extensive experience in production-grade data modeling.

• Modern Data Stack: Hands-on experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).

• ETL/ELT Proficiency: Experience with modern tooling such as dbt, Airflow, Census, or similar platforms.

• GTM Architecture: Deep understanding of Salesforce data models and common go-to-market system architectures.

• Communication: Ability to translate complex business concepts into durable data models and communicate them to both technical and non-technical partners.

Qualifications & Compensation

• Education: Bachelor’s degree in Computer Science, Data Science, or a related field (preferred).

• Experience: Proven track record of designing and operating production data pipelines.

• Salary Range: * NYC/SF/LA/Seattle: $158,400 – $198,000

  • Other US Locations: $142,600 – $178,200

  • Canada: $149,700 – $187,100 CAD

Diversity & Hiring Insights

Mercury utilizes Covey Scout, an Automated Employment Decision Tool (AEDT), to assist in the recruitment process. In alignment with New York City requirements, independent bias audits are conducted on such tools.

Covey Scout Bias Audit Data (Sample Summary)

According to typical audit reports for this tool category (e.g., NYC Local Law 144 compliance), the selection rates across different demographic groups are analyzed to ensure fairness. Below are representative statistics often found in these audits regarding the “Selection Rate” (the rate at which candidates are moved forward in the process):