Senior Data Engineer: AI Pipelines & Data Quality

Ciandt; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Health and dental insurance

Meal allowance

Childcare support

Paternity leave

Wellbeing program

Profit sharing

Life insurance

Learning platform

Job summary

CI&T is seeking an experienced Data Engineer to design, operate and optimize ETL/ELT pipelines connecting CRM (Veeva) to the Data Lake and Adobe Experience Platform. You will model analytical tables, ensure data quality and apply LGPD compliance across data flows.

Collaboration with Data Platform and architecture teams is essential. Strong production pipelines experience and advanced SQL, Python, and Databricks skills are required, along with English documentation proficiency.

Qualifications

• Production pipelines experience with SQL Python Databricks.
• Strong ability to model analytic tables and data governance.
• Experience applying LGPD to data workflows and privacy controls.
• Proficiency in technical documentation in English and Portuguese.

Responsibilities

• Design and operate ETL/ELT pipelines between CRM, Data Lake and AEP.
• Model analytical tables in the Data Lake (Databricks) and compute metrics.
• Implement data quality testing and reconciliation across sources.
• Apply LGPD-based classification, retention, and pseudonymization.
• Support architectural efforts in XDM modeling and identity strategy.
• Document pipelines, data dictionary and lineage; maintain runbooks.
• Sustain pipelines in production and investigate data incidents.

Skills

Advanced SQL

Python

Databricks

API data integration

Data quality testing

Data governance

English documentation

LGPD compliance

Tools

Veeva

AEP (Adobe Experience Platform)

Data Lake tooling

Job description

CI&T is seeking an experienced Data Engineer to design, operate and optimize ETL/ELT pipelines connecting CRM (Veeva) to the Data Lake and Adobe Experience Platform. You will model analytical tables, ensure data quality and apply LGPD compliance across data flows.

Collaboration with Data Platform and architecture teams is essential. Strong production pipelines experience and advanced SQL, Python, and Databricks skills are required, along with English documentation proficiency.