Senior Data Engineer

[Only Argentina]

We are looking for a Senior Data Engineer to join our teams!

The project builds an enterprise data platform for a healthcare client’s commercial and sales functions. The team curates domain-specific data models behind dashboards for Direct-to-Consumer (acquisition cost, funnel conversion, fulfillment, retention), Territory Insights (quota attainment, coverage, market penetration) and Inside Sales (lead velocity, deal progression, rep productivity), working with engineers and healthcare professionals.

Requirements

• 6+ years of data engineering experience, including 2+ years leading or managing engineering teams or projects.
• Deep hands-on experience extracting, modeling and transforming Salesforce (SFDC) objects and relational schemas for analytics.
• Strong command of dimensional modeling (Kimball methodology), star schemas, slowly changing dimensions (SCD) and enterprise data warehousing concepts.
• Advanced SQL: complex joins, query optimization, indexing, and data pipeline orchestration (e.g., Databricks DABs).
• Proficiency with cloud data platforms such as Databricks, plus modern transformation tools.
• Prior experience with commercial datasets: sales pipelines, DTC transactions, territory mapping or inside sales rep performance.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems or a related quantitative field, or equivalent practical experience.
• Builds and maintains the commercial data platform that turns raw sales, Salesforce and DTC data into curated, analytics-ready data models and semantic layers for analysts, marketers and executive leadership.

Job responsibilities

• Designs and optimizes Star/Snowflake schema data models and Gold-layer tables (medallion architecture) for commercial, sales and DTC analytics.
• Extracts and harmonizes Salesforce Sales Cloud and Service Cloud objects together with Direct-to-Consumer transaction data.
• Builds and automates ETL/ELT pipelines (SQL, Python, Spark, Databricks) into curated data marts.
• Develops semantic models and standardized metrics (Databricks metric views) as a single source of truth.
• Applies data governance, data quality testing and MDM principles; deploys code via Git-based CI/CD.

What we offer

Exciting Projects

Collaborative Environment

Work-Life Balance

Professional Development

Excellent Benefits