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Role Description

This role involves working closely with Forward Deployment Engineers (FDEs) to enable rapid ERP modernization and AI-driven transformation for enterprise clients. The focus of this role is data harmonization, cross-system integration, and pipeline development, ensuring that AI solutions and enterprise workflows are powered by clean, reliable, and well-structured data.

• Emphasizes ETL, relational schema modeling and mapping, joins, data cleaning, and pipeline logic for structured/tabular data.
• Includes a lightweight upstream MLOps component limited to structured datasets, which may involve distributed processing using PySpark or ML data engineering techniques.
• Requires deeper ERP-centric data understanding than a typical ML data engineering role.
• Strong generalist engineering skills to build scalable, production-grade pipelines are necessary.
• Candidates with SAP data expertise and modern data engineering or ML-enablement experience are ideal.

Key Responsibilities

• Data Harmonization: Integrate, reconcile, and standardize structured data across ERP, CRM, finance, and analytics systems.
• Cross-System Pipeline Architecture: Design and implement ETL/ELT pipelines that unify data across enterprise systems for AI-driven use cases.
• Data Transformation & Validation: Build logic to clean, transform, validate, and prepare structured/tabular datasets for operational and analytical workflows.
• Schema Interpretation: Analyze complex enterprise schemas and document entity relationships across systems.
• Pipeline Reliability: Monitor, troubleshoot, and optimize data pipelines to ensure consistent, high-quality delivery at scale.
• AI Enablement: Prepare structured datasets for multi-agent AI platforms, applying lightweight upstream MLOps practices where appropriate.
• Cross-Functional Collaboration: Work directly with FDEs, architects, and client teams to solve complex enterprise modernization challenges.
• Problem Solving Under Ambiguity: Decompose unclear requirements into clear, actionable technical solutions.

Qualifications

• Strong SQL skills, including complex joins and queries across multi-schema relational environments.
• Proficiency in Python or a comparable language for data processing, automation, and pipeline logic.
• Solid foundations in relational data modeling, schema mapping, and normalized/denormalized design.
• Experience working with enterprise systems such as SAP S/4HANA, Salesforce, finance systems, or cloud data warehouses.
• Hands-on experience building and maintaining ETL pipelines for structured/tabular data.
• Familiarity with distributed data processing (e.g., PySpark) and upstream MLOps concepts applied to structured datasets is a plus.
• Ability to operate effectively in fast-moving, ambiguous environments.
• Experience supporting analytics, ML pipelines, or AI workflows is preferred but not required.
• Demonstrated ability to navigate messy, fragmented enterprise data landscapes with inconsistent schemas and cross-system duplication.

Behavioral & Problem-Solving Expectations

• Comfortable working in a startup environment with high ownership and rapid iteration.
• Able to think like an engineer while navigating organizational and stakeholder dynamics.
• Communicates clearly and concisely, adjusting depth and detail to the audience.
• Operates effectively with incomplete information and adapts quickly to change.
• Uses AI-assisted tools thoughtfully to accelerate engineering productivity and solution delivery.