Data Engineer

Responsibilities:

• Architect and implement data lakehouse solutions to centralize and harmonize supply chain

and procurement data from multiple enterprise systems

• Design and deploy data ingestion pipelines for structured and unstructured data, including

ERP sources (SAP S/4HANA, Ariba), external market feeds, and technical documents

• Develop and maintain unified data models and taxonomies to support analytics and AI-driven

forecasting

• Build and optimize pipelines for processing unstructured data (PDFs, CAD files, regulatory

documents) into formats suitable for AI and RAG applications

• Manage and optimize vector databases to enable high-speed retrieval of engineering and

procurement data for generative AI tools

• Establish and enforce data lineage, traceability, and governance protocols to ensure data

integrity and compliance

• Implement and monitor data quality controls to validate completeness and accuracy of critical

datasets

• Collaborate with cross-functional teams to map enterprise data sources and define

requirements for AI and analytics use cases

• Optimize data workflows for secure, on-premise, and air-gapped environments, ensuring

efficient use of infrastructure

• Support the technical execution of foundational data platform initiatives within structured sprint

cycles

Skills:

• Expert proficiency in Python, SQL, and modern data engineering frameworks (Apache Spark,

Kafka, Airflow).

• Enterprise ERP: Strong experience extracting data from complex ERP environments,

specifically SAP S/4HANA and SAP Ariba. Familiarity with SAP BTP is a plus.

• Database Technologies: Deep understanding of Data Lakehouse architectures

(Databricks/Delta Lake), Relational Databases (PostgreSQL), and Vector Databases
(Weaviate/Milvus).

• Data Pipeline Development: Experience building pipelines for RAG solutions, Conversational

agents and classical ML models with tools like dbt, dagster, or prefect

• DevOps/DataOps: Proficiency with containerization (Docker, Kubernetes) and CI/CD pipelines

for deploying data workflows in secure environments.

• Experience: 7+ 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.

• Domain Knowledge: Experience in Supply Chain, Manufacturing, or Defense sectors is highly

desirable. Ability to understand "Bill of Materials" (BOM) structures and procurement lifecycles.

• Problem Solving: Ability to navigate the "Governance Collision" between agile data work and

rigid systems engineering requirements, ensuring data deliverables meet formal Stage Gate
reviews.

• Collaboration: Proven ability to work alongside Data Scientists and Backend Engineers to

define data schemas that support predictive modeling and AI agents.