Snowflake Data Engineer

Role Overview
The Data Engineer will be part of Analytics team, will build and maintain the reusable data assets that power
trusted reporting, analytics, and AI use cases acros. This role is responsible for moving critical business logic out
of isolated spreadsheets, one-off reports, and manual workflows into governed, scalable data layers in
Snowflake and dbt.
This role will work closely with Analytics, Data Engineering leadership, AI Engineering, and Business Unit
stakeholders to create reliable data pipelines, improve data quality, reduce manual reporting friction, and
support a scalable reporting architecture for the business.
Key Responsibilities
• Design, build, and maintain reusable data assets in Snowflake and dbt to support GTM analytics,
reporting, forecasting, pipeline inspection, seller productivity, channel analytics, and business
performance measurement.
• Develop scalable data models that standardize core GTM entities such as customers, prospects,
opportunities, sellers, territories, products, channels, activities, and pipeline.
• Move business logic out of individual reports, Excel files, and manual processes into governed,
reusable data layers.
• Create trusted data foundations that allow reporting and analytics teams to move faster with less
rework.
Data Pipeline Development
• Build reliable, well-documented data pipelines that ingest, transform, validate, and publish GTM data
for analytics consumption.
• Partner with enterprise data teams to source required data from CRM, sales, finance, product,
channel, and operational systems.
• Ensure pipelines are built for performance, maintainability, scalability, and long-term
• ownership.
• Monitor pipeline health, troubleshoot issues, and resolve data defects in partnership
• with upstream and downstream teams.
DBT and Snowflake Development
• Develop and maintain dbt models, tests, documentation, and transformation logic.
• Use Snowflake to create efficient, scalable, and trusted data structures for reporting,
• analytics, and AI use cases.
• Apply strong engineering practices including version control, modular design, peer
• review, testing, and documentation.
• Ensure data models are understandable, reusable, and aligned to approved KPI
• definitions and business rules.
Data Quality and Governance
• Implement data quality checks, validation rules, reconciliation processes, and
• exception handling to increase trust in GTM data.
• Partner with the GTM KPI Council and business translator role to ensure data
• definitions align with approved business metrics.
• Identify and resolve inconsistencies across reporting sources, metric definitions, and business logic.
• Help establish standards for naming, documentation, lineage, ownership, and data usage.
Reporting Architecture and Analytics Enablement
• Support the creation of a scalable reporting architecture that reduces reporting sprawl and manual
effort.
• Build data layers that enable Power BI and other reporting tools to consume trusted, consistent, and
reusable datasets.
• Partner with analytics and reporting teams to ensure dashboards and business- facing outputs are
built on governed data assets.
• Enable faster delivery of analytics by reducing dependency on custom extracts, manual joins, and one
off data preparation.
Partnership with DAAI, AI Engineering and Advanced Analytics
• Work with DAAI, AI Engineering and analytics teams to prepare trusted data assets for AI, machine
learning, forecasting, propensity models, seller recommendations, and other advanced analytics use
cases.
• Ensure data pipelines and models are structured to support both reporting and more advanced
analytical consumption.
• Help translate technical data requirements into scalable data solutions that can
support future GTM intelligence capabilities.
Required Qualifications
• Experience building data pipelines, data models, and analytics-ready datasets in Snowflake.
• Hands-on experience with dbt, including model development, testing, documentation, and
transformation logic.
• Strong SQL skills and experience working with complex business data.
• Experience designing reusable data assets for reporting, analytics, or business intelligence use cases.
• Understanding of data quality, data governance, lineage, documentation, and production support
practices.
• Ability to work with cross-functional teams including Analytics, RevOps, Sales Operations, Data
Engineering, AI Engineering, and business stakeholders.
• Strong problem-solving skills with the ability to simplify complex data issues and create scalable
solutions.
• Experience using version control and modern data engineering development practices.
Preferred Qualifications
• Experience with GTM, Sales, RevOps, CRM, pipeline, forecasting, seller productivity, channel, or
customer data.
• Familiarity with Salesforce or similar CRM platforms.
• Experience supporting Power BI, Tableau, or other enterprise reporting tools.
• Experience building semantic or curated data layers for business users.
• Experience working in an enterprise environment with shared data platforms, centralized engineering
teams, and business-facing analytics teams.
• Familiarity with AI/ML data preparation, feature development, or analytical model support.