Senior Analytics Engineer, Revenue Operations

DNSFilter is a rapidly growing company dedicated to creating a safer internet through innovative network security solutions. They are seeking a Senior Analytics Engineer, Revenue Operations, to own and scale the RevOps data domain, ensuring high-quality analytics-ready data that powers decision-making across various teams.

Responsibilities:

• Own the RevOps Data Domain
• Architect the RevOps/BizOps data zone within our Data Mesh as a high-quality, documented "product" and the authoritative source for revenue data
• Establish clear ownership and governance for core RevOps datasets and metrics
• Build & Scale Analytics Foundations
• Own the end-to-end design and maintenance of analytics solutions powering GTM systems and decision-making
• Partner with Data Engineering to transform raw data from internal databases and APIs into analytics-ready models
• Build and optimize ELT pipelines using dbt Cloud and Amazon Athena, ensuring high performance and data availability
• Own Data State, History, and Performance
• Manage data evolution and history using dbt snapshots and incremental models for trend analysis
• Write performant, cost-aware SQL for distributed query engines, managing datasets exceeding billions of rows
• Prioritize scalable, clear data models over complex, fragile workarounds
• Define, Govern & Operationalize Metrics
• Maintain a dbt semantic layer to serve as the single source of truth for business definitions
• Steward a centralized data dictionary to ensure consistent reporting across GTM systems
• Collaborate with Finance, Product, and GTM teams to define and evolve shared metrics, managing documentation and versioning
• Model Revenue Data for Downstream GTM Systems
• Design data models consumable by GTM tools like Salesforce, HubSpot, and Zendesk
• Expose clean, documented datasets for reuse across automation and operational workflows
• Partner with GTM Systems to align analytical models with operational logic and minimize metric drift
• Own BI Reporting & Visualization
• Design and maintain RevOps dashboards in our BI tool using governed, tested datasets
• Translate stakeholder needs into scalable, self-service reporting tools
• Set standards for dashboard design, usability, and reporting governance
• Drive Accuracy, Simplicity & Trust
• Validate data end-to-end via SQL and source-system analysis to resolve discrepancies
• Favor readable, maintainable SQL over over-engineered abstractions to reduce technical debt
• Address data issues at the source by partnering with upstream owners rather than applying brittle downstream fixes
• Enable the Business & Look Forward
• Provide reliable, well-documented analytics dependencies for the GTM Systems team
• Utilize AI-assisted tools to accelerate dbt development, testing, and documentation
• Pilot AI-driven approaches to improve data quality and operational efficiency

Requirements:

• 5+ years of experience in analytics engineering or data engineering, specifically supporting GTM, RevOps, or BizOps functions
• Expert dbt knowledge, including advanced use of incremental strategies, snapshotting, and modular project structure—you know when to use a macro and when not to
• Deep proficiency in SQL with experience optimizing queries for modern distributed warehouses (e.g., Amazon Athena, BigQuery, Snowflake), including partitioning and cost optimization
• Hands-on experience designing and maintaining analytics-ready data models and ELT pipelines from application and operational data sources
• Experience implementing or working with semantic layers or governed metrics frameworks (e.g., dbt semantic layer or equivalent)
• A meticulous, almost obsessive approach to data accuracy—you aren't satisfied until the numbers tie out 1:1 and you can prove it
• Demonstrated ability to reconcile complex datasets across systems and identify root causes of discrepancies
• A strong 'do it right' mindset, including the ability to push back on unscalable requests and prioritize durable solutions over short-term fixes
• Understanding of data mesh principles, domain ownership, and the discipline required to maintain a standalone analytics data zone
• Strong communication skills and comfort level in influencing both technical and non-technical stakeholders
• Direct experience working on a Revenue Operations team
• Experience supporting Sales, Marketing, Customer Support, and Customer Success analytics
• Experience introducing or maturing a centralized data dictionary and driving organizational adoption of governed metrics
• Exposure to data mesh or domain-oriented data ownership models in production environments
• Experience applying AI-assisted development tools to analytics engineering workflows (SQL, dbt, testing, documentation, refactoring)
• Familiarity with revenue lifecycle metrics (pipeline, conversion rates, ARR/MRR, churn, expansion, forecasting)