Data Engineer - Customer Success Analytics

Description:


The Data Engineer will own and strengthen the data foundation that powers Customer Success for Evergreen//One, our subscription offering.
This role is responsible for building and maintaining scalable data pipelines, improving and restructuring Snowflake architecture, and ensuring subscription data is clean, trusted, and analytics-ready. You will play a critical role in resolving recurring data inconsistencies surfaced in Gainsight and Tableau by developing well-modeled, reliable datasets that serve as a clear source of truth.
This is a hands-on role focused on data ownership, modeling, quality, and performance - enabling scalable automation and future AI-driven solutions across Customer Success.
WHAT YOU'LL DO
Design, build, and maintain ETL/ELT pipelines from Salesforce, Gainsight, and other operational systems into Snowflake.
udit, normalize, and restructure existing Snowflake tables and views to improve clarity, consistency, and performance.
Develop clean, analytics-ready data models that power Tableau dashboards, Gainsight workflows, and executive reporting.
Translate business logic (ARR, renewals, churn, consumption, health scoring) into structured, documented data definitions.
Investigate and resolve root causes of reported data discrepancies and implement durable fixes.
Optimize query performance, warehouse utilization, and overall Snowflake efficiency.
Implement data validation, monitoring, and quality controls to improve trust in reporting.
Document data lineage, transformations, and definitions to improve transparency and governance.
Partner closely with Senior Data Analysts, Customer Success, and Ops to ensure scalable, reusable datasets.
Prepare structured datasets that support automation initiatives and future AI use cases.


WHAT YOU'LL BRING
Minimum Required Experience & Skills
Bachelor's degree in Computer Science, Computer Engineering, Information Systems, or equivalent practical experience
2+ years of experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala or others.)
2+ years of experience in designing, developing, and maintaining robust data models from structured and unstructured sources
Experience proactively identifying opportunities to improve ETL & dashboard performance and cost
2+ years of experience where the primary responsibility involves working with data. This could include roles such as data analyst, data scientist, data engineer, analytics engineer, or similar positions
Experience with data warehouse technologies (Snowflake, BigQuery, Spark, etc) and data build tools such as DBT.
Experience in Git/GitHub and branching methodologies, code review tools, CI tools, JIRA, Confluence.
Strong understanding of data modeling principles (normalization, dimensional modeling, schema design).
Experience proactively identifying and improving data pipeline or dashboard performance.
Strong analytical and problem-solving skills, including experience investigating and resolving data inconsistencies.
Nice to have
Experience working with Salesforce data models (Accounts, Opportunities, Contracts, Subscriptions).
Experience with Gainsight, Tableau, and SnapLogic.
Experience supporting SaaS or subscription-based business models (ARR, renewals, consumption).
Exposure to automation, predictive modeling, or AI-related data preparation.
Experience implementing data governance, access controls, and documentation standards.
Experience with server-side languages like TypeScript/Node.JS/Python/Kotlin
Experience with RESTful API design
MSP NOTES
Fully remote candidates but preference for candidates in the San Francisco Bay Area
Fixed end date: January 29, 2026 but possibility of extension depending on performance and business needs
Max Bill Rate: (USD)***/hr