AI Data Engineer- INTL LATAM

Data Pipeline Development (40%) *Design, build, and maintain ETL/ELT pipelines from enterprise systems (Oracle EPM, OHM ERP, Salesforce, Power BI) *Create reusable data connectors and transformation layers that serve multiple AI projects simultaneously *Implement data quality monitoring, alerting, and automated refresh scheduling *Build and maintain the data infrastructure on Azure (Data Factory, SQL Database, Blob Storage) Enterprise Data Integration (25%) *Collaborate with the Data Architecture team on data warehouse access, governance, and standards *Navigate data access processes for cross-business-unit projects (Finance, Manufacturing, Sales, Field Ops) *Maintain data contracts and SLAs between source systems and AI applications *Serve as the liaison between AI projects and enterprise data systems AI/ML Data Support (20%) *Prepare and maintain datasets for AI model training and inference *Build data validation frameworks to ensure model input quality *Support both real-time and batch data requirements for AI services *Leverage AI agents and automation tools for data preparation (e.g., automated data cleansing, schema detection) Documentation & Compliance (15%) *Document data lineage, schemas, and transformation logic for all CoE data assets *Ensure compliance with SSB data governance policies *Maintain a data catalog covering all CoE data assets and their enterprise source systems

• Support audit and compliance requirements for AI solutions handling financial or sensitive data

SQL (Advanced) Expert Primary language for data warehouse, ODS, Oracle EPM, and OHM ERP queries Python- Strong ETL scripts, data transformation, integration with AI/ML pipelines ETL/ELT Tools Strong Azure Data Factory, dbt, or equivalent orchestration tools Cloud Data (Azure)- Intermediate+ SSB infrastructure runs on Azure - storage, ADF, SQL Database, Blob Data Modeling Strong Dimensional modeling, data structures for analytics and AI consumption API Development- Intermediate REST APIs for data service layers and cross-system integration AI/ML Concepts Familiarity Understanding model data requirements, feature engineering, and AI pipeline patterns ERP Systems- Familiarity OHM or similar manufacturing/finance ERP systems - understanding data structures and export patterns

Nice to Have Skills & Experience

Experience with Oracle EPM, Oracle Cloud, or similar financial planning systems * Background in manufacturing or CPG data environments * Experience building data pipelines that serve AI/ML models in production * Familiarity with Salesforce data integration * Experience with document processing pipelines (OCR, PDF extraction) * Knowledge of data governance frameworks and data quality tools

$45/hr to $55/hr

Exact compensation may vary based on several factors, including skills, experience, and education., Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.

The AI Center of Excellence has built a portfolio of over 100 AI opportunities across a client of IG, with 10+ active projects delivering validated prototypes to business stakeholders. The CoE has proven its ability to rapidly prototype AI solutions - multiple projects have achieved validated results within weeks. However, the consistent pattern across all active projects is the same: the path from prototype to production requires dedicated data engineering capabilities that the CoE does not currently have. Today, data preparation consumes approximately 45-55% of the AI Solution Architect's time - time that should be spent on architecture, stakeholder engagement, and new opportunity development. Every prototype relies on manual data exports (Excel, CSV) rather than automated pipelines connected to enterprise systems. This bottleneck limits both the number of projects the CoE can support and the speed at which validated prototypes can reach production.