Data Engineer – Customer Success Analytics

About the role

We are looking for a Data Engineer focused on Customer Success Analytics. The role will design, build and maintain the data pipelines and models that power our Customer Success team’s reporting and automation initiatives. You will work in a hybrid setting, collaborating with analysts, operations and product stakeholders to turn raw data into reliable, actionable insights.

Key responsibilities

• Design, develop and maintain ETL/ELT pipelines from systems such as Salesforce into Snowflake or other cloud data warehouses.
• Audit, normalize and restructure existing tables, views and data models to improve performance and usability.
• Create scalable, analytics‑ready data models that support dashboards, reporting and operational workflows.
• Translate business metrics (ARR, renewals, churn, consumption, customer health) into documented data definitions.
• Investigate data discrepancies, identify root causes and implement long‑term fixes.
• Optimize query performance and overall data warehouse efficiency.
• Implement data validation, monitoring and quality controls to ensure accuracy.
• Document data lineage, transformations and definitions for governance.
• Collaborate with Data Analysts, Customer Success and Operations to deliver reusable datasets.
• Prepare structured data for automation, reporting enhancements and future AI use cases.

Required profile

• Bachelor’s degree in Computer Science, Engineering, Information Systems or equivalent experience.
• 2+ years of experience in data engineering, analytics engineering or a related role.
• Hands‑on experience building data pipelines and data models in a cloud environment.
• Ability to work cross‑functionally and communicate technical concepts clearly.

Required skills

• Strong SQL proficiency.
• Experience with ETL/ELT processes and data modeling.
• Programming in Python (or Scala, C#).
• Hands‑on with Snowflake, BigQuery or Spark.
• Familiarity with dbt (data build tool).
• Version control using Git/GitHub.
• Understanding of dimensional modeling, normalization and schema design.
• Experience with Salesforce data models.