Mercury
Finance / FinServ
RevenueTechnologyDataStrategy&OperationsLead
“Revenue Technology - Data Strategy & Operations Lead at Mercury. Skills: data strategy, data operations, data modeling, SQL, modern data stacks, ETL / ELT tooling. Own the definition, structure, and reliability of data originating from revenue platforms (e.g., Salesforce, GTM tools, automation systems). Serve as the primary decision owner for GTM-sourced tables and views used for revenue execution, forecasting inputs, lifecycle tracking, and signal-based workflows”
Industry & Context.
translate business concepts into durable, well-structured data models; proactively identifying fragile or redundant transformations; identifying opportunities to automate manual or error-prone data workflows
What They're Looking For.
Must Have
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, Be able to translate business concepts into durable, well-structured data models
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 You'll Do.
and reliability of data originating from revenue platforms (e.g.
Serve as the primary decision owner for GTM-sourced tables and views used for revenue execution
and signal-based workflows
Design and evolve core GTM data models across Salesforce
Partner with Data Engineering to align GTM schemas with enterprise data models and define clear data contracts between source systems and downstream consumers
Partner with Data Science / Analytics to ensure revenue data is interpretable
and reflects how the business actually operates
Own clarity around data ownership boundaries
and escalation paths when upstream or downstream changes impact revenue integrity
Define and uphold data quality
and documentation standards for revenue platforms
Monitor and improve pipeline reliability
proactively identifying fragile or redundant transformations
Identify opportunities to automate manual or error-prone data workflows and reduce operational overhead
Act as a data thought partner to Platforms & Infrastructure
and Security — advising on feasibility
and sequencing for data-heavy initiatives
How You'll Work.
Team & Collaboration
Partner closely with Data Engineering, Data Science, Solution Architecture, Platform Engineering.; Partner with Data Engineering to align GTM schemas with enterprise data models and define clear data contracts between source systems and downstream consumers; Partner with Data Science / Analytics to ensure revenue data is interpretable, statistically sound, and reflects how the business actually operates; Act as a data thought partner to Platforms & Infrastructure, Revenue Operations, Analytics, and Security
Communication Scope
Communicate clearly with both technical and non-technical partners
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