Data Engineer – Customer Success Analytics

Location: Hybrid, Ultralag, Heredia

Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience)

Experience: 2+ years in data engineering, analytics engineering, or similar roles

No of Openings: 1

Summary

The Data Engineer – Customer Success Analytics is responsible for building and maintaining the data infrastructure that supports Customer Success operations and decision-making. This role focuses on developing scalable data pipelines, improving data models, and ensuring high-quality, reliable datasets for reporting and analytics. The ideal candidate is hands-on, detail-oriented, and passionate about transforming complex data into actionable insights that drive customer outcomes, operational efficiency, and future automation initiatives.

Roles And Responsibilities

• Design, build, and maintain ETL/ELT pipelines from systems such as Salesforce and other operational platforms into data warehouses (e.g., Snowflake).
• Audit, normalize, and restructure existing data models, tables, and views to improve performance, consistency, and usability.
• Develop clean, scalable, and analytics-ready data models to support dashboards, reporting, and operational workflows.
• Translate business requirements (e.g., ARR, renewals, churn, consumption, customer health) into structured and well-documented data definitions.
• Investigate and resolve data discrepancies by identifying root causes and implementing long-term fixes.
• Optimize query performance, data processing, and overall data warehouse efficiency.
• Implement data validation frameworks, monitoring processes, and quality controls to ensure data accuracy and reliability.
• Document data lineage, transformations, and definitions to support governance and transparency.
• Collaborate with Data Analysts, Customer Success teams, and Operations to build scalable and reusable datasets.
• Prepare structured datasets to support automation initiatives, reporting improvements, and future AI-driven use cases.

• 2+ years of experience working with data in roles such as Data Engineer, Data Analyst, Analytics Engineer, or similar.
• Strong SQL skills and experience with ETL/ELT processes and data modeling.
• Experience with at least one programming language (e.g., Python, Scala, C#, or similar).
• Hands-on experience with data warehouse technologies (e.g., Snowflake, BigQuery, Spark).
• Familiarity with data build tools such as DBT.
• Experience with version control tools (Git/GitHub) and development workflows.
• Strong understanding of data modeling principles (normalization, dimensional modeling, schema design).
• Proven ability to identify and improve data pipeline and reporting performance.
• Strong analytical and problem-solving skills, particularly in resolving data inconsistencies.
• Ability to work cross-functionally and communicate technical concepts clearly.

• Experience working with Salesforce data models (Accounts, Opportunities, Contracts, Subscriptions).
• Familiarity with tools such as Tableau, Gainsight, or similar analytics platforms.
• Experience supporting SaaS or subscription-based business models (ARR, renewals, churn, consumption).
• Exposure to automation, predictive analytics, or AI-related data preparation.
• Experience with data governance, access control, and documentation standards.
• Knowledge of REST APIs and server-side technologies (e.g., Node.js, TypeScript, Python).