Principal Engineer, Data Engineering
Core
Design and own end-to-end data architecture to build scalable, resilient systems enabling modern AI and analytics solutions for diverse applications.
Role type
Principal Engineer, Data Engineering
Builds
Scalable data platforms, AI/ML data pipelines, and analytics solutions
Domain
Semiconductor / Data Engineering / AI/ML
Deliverable
production ML models | infrastructure
Required skills
Distributed systems architecture, Streaming systems design, Data modeling, ETL development, Cloud-native data architecture, Data governance, Observability frameworks, Machine learning concepts for data workflows, Technical leadership, Stakeholder collaboration
Preferred skills
None stated
Technologies
Spark, Kubernetes, Terraform, Kafka, Pub Sub, Airflow, Hadoop, Informatica, Talend, SQL, Python, Oracle, SQL Server, Vertica, Looker, AtScale, Tableau, Business Objects, GCP (BigQuery, Dataflow), Azure (Synapse, Fabric), AWS (Redshift, Glue), Grafana, Datadog
Responsibilities
Develop strategies and design end-to-end data architecture; Define engineering best practices for data modeling and ingestion; Drive Data Quality, Observability, and Governance frameworks; Provide technical leadership and mentor data engineers; Collaborate with AI/ML engineers to enable model training and deployment; Lead large-scale complex data projects; Serve as SME in strategic data domains; Translate business problems into scalable technical designs; Influence design decisions across teams to capture AI/ML use cases.
Seniority
Principal, hands-on IC with strategic influence and mentorship