Data Application Engineer, Enterprise Data Management

About the job

Responsibilities

• Develop a deep understanding of the chip supply chain, including part development, PLM systems, material master configuration, and planning master data, as well as the ontology/knowledge graph structure for agentic AI projects.
• Develop, test, and maintain data pipelines, APIs, and agent integrations for the Planning Data Management Tool (PDMT) and related supply chain data solutions.
• Architect and implement enterprise Master Data Management (MDM) and Reference Data Management (RDM) solutions for material master, BOM, customer, supplier, and reference data.
• Collaborate with engineering, business, and IT groups to translate complex supply chain and semiconductor requirements into scalable, governed data solutions.
• Design and implement data integration and pipeline architectures, including real-time, batch, and event-based pipelines for large-scale datasets.
• Lead the creation of enterprise data governance capabilities, including business glossaries, data catalogs, lineage tracking, and stewardship frameworks.
• Establish an AI-enabled data observability layer to proactively monitor data quality, lineage, and operational health.
• Build and manage enterprise-grade AI agents for EDM data observability, automating workflows and enabling self-healing of data from various sources.
• Develop canonical data models, standardized taxonomies, and process-aligned data structures for consistent and interoperable enterprise data.

Requirements

• Over 8 years of experience in enterprise data architecture and engineering, MDM, RDM, and scalable data platform solutions, preferably in supply chain or semiconductor manufacturing.
• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, Industrial Engineering, or equivalent experience.
• Hands-on expertise with Informatica Intelligent Data Management Cloud (IDMC), including MDM, CDI, CAI, CDGC, IDQ, Reference 360, Metadata Management, and Data Catalog.
• Extensive experience with Databricks lakehouse architecture (Spark, PySpark, Delta Lake), scalable data pipeline frameworks, and building enterprise data platforms with a focus on data governance, quality, and stewardship.
• In-depth understanding of supply chain and manufacturing data domains, including Material Master, BOM, Product Data, Supplier Data, and Reference Data.
• Ability to manage procedural and functional elements of data domains, understand detailed process flows and business rules, and incorporate agentic AI into data applications.
• Advanced skills in data modeling, canonical data construction, enterprise terminology collections, metadata catalogs, and lineage frameworks.
• Experience developing enterprise data solution architectures, featuring ETL/ELT pipelines, API integrations, microservices, and event-driven data patterns.
• Experience integrating enterprise data platforms with ERP and PLM systems (e.g., SAP S/4HANA, SAP MDG, SAP IBP, SFDC).
• Understanding of advanced technology platforms (e.g., ChatGPT, Copilot, Gemini, Claude) and practical experience crafting agentic AI workflows (LLM-based agents, RAG, orchestration) for data quality, alerting, and observability.
• Hands-on knowledge of semiconductor chip supply planning, including chip family and part development, PLM relationships, and planning master data within SAP IBP and Anaplan.

Benefits

• Attractive salaries
• Extensive benefits package