Senior Analytics Engineer, GTM

Role Overview

Zywave is seeking a Senior Analytics Engineer, GTM to own and improve the revenue data foundation across Salesforce, Snowflake, and other GTM systems. This role focuses on data cleanliness, source-of-truth alignment, hierarchy integrity, metric logic, reconciliation, and reportable data models.

This senior individual contributor will work deeply in Snowflake and SQL, understand Salesforce data structures, consolidate data from multiple systems, and create trusted datasets for reporting and analytics. The role is distinct from a reporting analyst: it owns the underlying data layer that makes reporting accurate, scalable, and trusted.

Reporting Relationship

This role will report to the VP, Revenue Operations

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Key partners include Revenue Operations, Sales Operations, Client Success Operations, Finance, Marketing Operations, Business Applications, Data / BI, Sales and CS leadership

Primary Responsibilities

GTM Source-of-Truth Data Model

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Build and maintain trusted GTM data models in Snowflake that support reporting across Sales, CS, Finance, Marketing Operations, and Revenue Operations.

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Consolidate Salesforce and other GTM system data into a single, reportable source of truth.

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Create governed datasets for pipeline, bookings, forecast, renewals, expansion, churn, hierarchy, product, territory, and sales performance reporting.

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Define transformation logic, business rules, joins, filters, exclusions, and documentation needed to make data usable and repeatable.

Salesforce and Snowflake Reconciliation

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Reconcile Salesforce and Snowflake data so core revenue metrics match across systems.

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Investigate differences between Salesforce reports, Snowflake tables, BI dashboards, and Finance reporting.

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Identify whether discrepancies stem from source-system issues, integration failures, transformation logic, timing differences, field mapping, hierarchy logic, or reporting filters.

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Build QA checks and reconciliation processes that proactively flag mismatches before they reach leadership reporting.

Data Cleanliness, Standardization, and Governance

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Lead data cleanup and standardization across GTM systems and reporting layers.

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Identify duplicate, incomplete, inconsistent, stale, or misclassified revenue data.

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Create repeatable rules for data normalization, account hierarchy, territory assignment, product mapping, opportunity classification, renewal logic, and lifecycle status.

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Maintain mapping tables, data dictionaries, metric definitions, lineage documentation, and QA logic.

Multi-System Consolidation

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Consolidate data from Salesforce and GTM sources such as marketing automation, customer success, sales engagement, forecasting, product usage, enrichment, quote/order, or finance platforms.

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Understand ingestion pipelines, sync timing, API limitations, and transformation dependencies in partnership with technical teams.

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Turn raw and inconsistent source data into reliable analytical datasets with clear lineage from source systems to final reporting tables.

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Reduce manual pulls, spreadsheet workarounds, and conflicting reports by improving the trusted data foundation.

Reporting Enablement, AI

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Build datasets, semantic layers, and curated views that allow analysts and business users to create trusted dashboards.

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Validate that reporting outputs match approved source-of-truth logic before dashboards are published.

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Use AI tools to accelerate SQL development, QA checks, documentation, anomaly detection, data mapping, and automation.

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Support U.S.-based stakeholders during U.S. business hours and translate technical data issues into transparent business language.

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Proactively flag risks, gaps, inconsistencies, and tradeoffs before they impact reporting.

What Success Looks Like

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Better alignment between Salesforce, Snowflake, BI dashboards, and Finance reporting.

Zywave | Confidential

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A clearer GTM source of truth for pipeline, bookings, forecast, renewals,



expansion, churn, territory, hierarchy, and customer reporting.

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Fewer conflicting reports and fewer manual reconciliation exercises.

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Cleaner account hierarchy, ownership, product, territory, and opportunity classification logic.

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Better documentation of lineage, metric definitions, transformation rules, caveats, and automated data-quality checks.

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Improved trust in leadership reporting and operating cadences.

Required Qualifications

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5+ years in analytics engineering, data engineering, data operations, BI engineering, RevOps analytics, or a related data role.

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Strong SQL skills and hands-on experience in Snowflake or a similar cloud data warehouse.

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Experience building curated reporting tables, analytical datasets, data models, semantic layers, or BI-ready views.

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Strong understanding of Salesforce data structures, including accounts, leads, contacts, opportunities, products, quotes, activities, users, roles, and hierarchy-related data.

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Experience reconciling data across multiple systems and resolving source-of-truth conflicts.

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Experience with data quality, governance, lineage, documentation, metric definition, QA, troubleshooting, and root-cause analysis.

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Ability to translate business requirements into technical data logic

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Ability to work U.S. business hours.

Preferred Qualifications

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Experience in B2B SaaS, insurtech, fintech, HR tech, benefits technology, broker technology, carrier technology, or another workflow-heavy software environment.

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Experience with transformation frameworks and BI tools such as Tableau, Power BI, Sigma, Looker, Domo, or similar platforms.

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Experience with GTM systems such as Salesforce, LeanData, Marketo, Clari, Gong, Outreach, Salesloft, Gainsight, CPQ, order management, or data enrichment tools.

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Experience supporting pipeline, bookings, renewal, churn, customer lifecycle, territory, account hierarchy, or revenue performance reporting.

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Experience creating data dictionaries, source-to-target mappings, QA checks, transformation documentation, metric governance, and AI-assisted data workflows

📌 GTM Senior Analytics Engineer (Maharashtra)
🏢 Zywave
📍 Maharashtra