Principal Graph Data Engineer- R&D IT

Summary: As a Senior data engineer you will directly impact the cost efficiency and speed of NXP’s New Product Introductions, as you will be responsible for the quality of the data and business insights that enable correct R&D business decisions. Together with your data engineering team and the R&D data scientists, you shape the future of the R&D data analytics platform. Your solutions delight R&D business with a topnotch self-service fully automated cloud-based data platform.   What you will do as a Data Engineer at NXP As part of the data engineering team, you will help shape and evolve the R&D data analytics platform. You will work closely with colleagues to explore new ideas, innovate on existing capabilities, and drive the platform forward through collaboration and technical excellence.   Your key responsibilities Partner with business stakeholders, data consumers, and source-system owners to understand analytical and AI use cases and translate graph-based data into consumable relational datasets. Design and implement graph-to-relational data models that enable graph-derived insights to be leveraged within Databricks, reporting platforms, and enterprise analytics solutions. Develop and maintain scalable ETL/ELT pipelines that extract, transform, and integrate graph data into the enterprise data lake and Lakehouse ecosystem. Collaborate with source-system teams to onboard new graph datasets and establish reliable data integration patterns. Design and implement graph data models, ontologies, and semantic structures where required to represent complex relationships across enterprise domains. Optimize graph extraction, transformation, and loading processes for performance, scalability, and data quality. Collaborate with Data Engineers, Data Architects, Data Scientists, and AI teams to make graph data broadly accessible across the enterprise. Apply graph analytics and graph-based techniques to uncover relationships, dependencies, lineage, and business insights. Provide guidance and best practices on graph technologies, graph data modeling, and graph-to-relational integration patterns.   What you bring Must have Proven experience designing and implementing enterprise graph data models and graph-based data solutions. Strong understanding of graph technologies and modeling approaches, including Property Graphs (LPG), RDF, ontologies, and semantic modeling. Experience with graph databases such as Neo4j, Amazon Neptune, Dydra, or equivalent technologies. Hands-on experience with graph query languages such as Cypher, SPARQL, Gremlin, or similar. Strong software engineering skills in Python. Extensive experience building ETL/ELT pipelines and data integration solutions using technologies such as Apache Spark, Databricks, Airflow, or similar platforms. Experience transforming and integrating graph data into relational, analytical, and Lakehouse data models. Strong understanding of data modeling techniques for analytics,…