Senior Data Engineer

Overview

Job Summary

Key Responsibilities

• Design and deploy defense-grade Data Lakehouse architectures to serve as the Single Source of Truth for AI-driven Supply Chain Intelligence, ensuring high-performance pipelines and systems for Intelligent Supply Chain forecasting and generative AI tools.
• Lead the technical execution of Workstream 1: Data & Platform Foundations, mapping rigid enterprise systems (e.g., SAP S/4HANA, Ariba) and collaborating with cross-functional teams to integrate and process complex unstructured data (technical drawings, regulatory text) for AI applications.
• Architect and deploy ingestion pipelines to extract high-volume transactional data from ERP systems like SAP S/4HANA, Ariba, and PLM, ensuring near real-time availability for forecasting models and AI agents.
• Build connectors for external market intelligence feeds (e.g., S&P Global, Orbis, EcoVadis) to enrich internal procurement data with macroeconomic and geopolitical signals for enhanced decision-making.
• Design and implement a standardized procurement data model and taxonomy across multiple entities, harmonizing fragmented datasets into a cohesive analytics layer.
• Engineer pipelines to ingest, process, and transform unstructured technical data (PDF tender documents, CAD metadata, historical CONOPS) into vector-ready formats for Retrieval-Augmented Generation (RAG) applications.
• Manage and optimize Vector Databases (e.g., Weaviate) to store embeddings of archival proposals and engineering snippets, ensuring high-speed retrieval for AI drafting assistants and generative tools.
• Establish data lineage and traceability protocols to link requirements to physical components, supporting Model-Based Systems Engineering (MBSE) and the Digital Thread implementation.
• Implement Role-Based Access Control (RBAC), audit logging, and data redaction policies to ensure compliance with export controls and strict on-premise security requirements.
• Deploy automated data quality frameworks to validate Bill of Materials (BOM) completeness and cost data accuracy before ingestion into AI models.
• Optimize data pipelines for on-premise GPU clusters and air-gapped environments, ensuring efficiency and performance within existing infrastructure constraints.
• Operate within a structured Sprint Zero environment to ensure data lineage, security, and governance meet defense-grade standards.

Qualifications And Experience

• 5+ years of experience in Data Engineering, with at least 2 years focused on building pipelines for Machine Learning or Generative AI applications in an enterprise setting.
• Expert proficiency in Python, SQL, and modern data engineering frameworks including Apache Spark, Kafka, and Airflow.
• Strong experience extracting data from complex ERP environments, specifically SAP S/4HANA and SAP Ariba, with familiarity in SAP BTP as a plus.
• Deep understanding of Data Lakehouse architectures (Databricks/Delta Lake), Relational Databases (PostgreSQL), and Vector Databases (Weaviate/Milvus).
• Experience building pipelines for RAG solutions, Conversational AI agents, and classical ML models using tools such as dbt, dagster, or prefect.
• Proficiency with containerization (Docker, Kubernetes) and CI/CD pipelines for deploying data workflows in secure environments.
• Experience in Supply Chain, Manufacturing, or Defense sectors, with the ability to understand 'Bill of Materials' (BOM) structures and procurement lifecycles.
• Ability to navigate the governance challenges between agile data work and rigid systems engineering requirements, ensuring data deliverables meet formal Stage Gate reviews.
• Proven ability to collaborate with Data Scientists and Backend Engineers to define data schemas that support predictive modeling and AI agents.

Technical Skills

• Expert proficiency in Python and SQL for data engineering tasks
• Strong experience with modern data engineering frameworks, including Apache Spark, Kafka, and Airflow
• Deep expertise in extracting and processing data from complex ERP environments, specifically SAP S/4HANA and SAP Ariba, with familiarity in SAP BTP as a plus
• Comprehensive understanding of Data Lakehouse architectures, such as Databricks and Delta Lake, for scalable and secure data storage
• Experience with relational databases, including PostgreSQL, and vector databases like Weaviate and Milvus for AI-driven applications
• Proven ability to develop data pipelines for Retrieval-Augmented Generation (RAG) solutions, conversational agents, and classical machine learning models using tools like dbt, Dagster, or Prefect
• Proficiency in containerization technologies, including Docker and Kubernetes, and experience implementing CI/CD pipelines for secure data workflow deployment
• Knowledge of defense-grade security protocols, including Role-Based Access Control (RBAC), audit logging, and data redaction policies for compliance with export controls and on-premise security requirements
• Experience building and optimizing pipelines for high-performance GPU clusters and air-gapped environments
• Familiarity with automated data quality frameworks to validate critical datasets such as Bill of Materials (BOM) and cost data for AI model accuracy
• Ability to design and implement standardized procurement data models and taxonomies to harmonize fragmented datasets across multiple entities
• Experience engineering pipelines for unstructured data ingestion, including PDF tender documents, CAD metadata, and historical CONOPS, transforming them into vector-ready formats for AI applications
• Knowledge of Model-Based Systems Engineering (MBSE) principles and the ability to establish data lineage and traceability protocols for the Digital Thread

About Our Sister Company

Role Overview

Core Objectives

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