Mid-level Data Engineer – Customer Success Analytics

About the role

The Data Engineer – Customer Success Analytics builds and maintains the data infrastructure that powers Customer Success operations. You will design scalable pipelines, improve data models, and ensure reliable datasets for reporting and future AI initiatives.

Key responsibilities

• Design, build, and maintain ETL/ELT pipelines from systems such as Salesforce into Snowflake or similar warehouses.
• Audit, normalize, and restructure existing tables and views to boost performance and usability.
• Develop clean, analytics‑ready data models supporting dashboards, reporting, and operational workflows.
• Translate business metrics (ARR, renewals, churn, customer health) into documented data definitions.
• Investigate data discrepancies, implement long‑term fixes, and optimize query performance.
• Implement validation frameworks, monitoring, and quality controls.
• Document data lineage and transformations for governance.
• Collaborate with analysts, Customer Success, and Operations to create reusable datasets.

Required profile

• 2+ years of experience in data engineering, analytics engineering, or related roles.
• Strong analytical mindset with a focus on data quality and performance.
• Ability to work cross‑functionally and communicate technical concepts clearly.

Required skills

• SQL
• ETL/ELT processes
• Python (or Scala, C#)
• Snowflake, BigQuery, or Spark
• DBT
• Git/GitHub
• Data modeling (normalization, dimensional modeling)
• Salesforce data structures