Principal Technical Lead, Software Engineering; Data Infrastructure - North America Software Center

Join TSMC Washington and help power the future of technology. At TSMC, we don't just make semiconductors; we innovate to transform industries and enhance lives. As the world’s leading semiconductor foundry, we partner with top tech companies to drive advancements in industries such as healthcare, automotive, consumer electronics, and renewable energy. At TSMC Washington, you'll thrive where innovation meets precision manufacturing, and integrity guides our high standards and customer trust.

Our visionary leaders collaborate with clients to achieve groundbreaking results, ensuring our leadership in the semiconductor sector. Explore career opportunities with TSMC Washington and join a company with a commitment to excellence and innovation.

We are seeking a talented and experienced Data Infrastructure Tech Leader to help design, optimize, and manage TSMC large-scale data infrastructure.

As the Principal Technical Lead – Data Infrastructure, you will define the technical vision, architecture, and roadmap for TSMC's enterprise data platform.

You will architect scalable data infrastructure that powers analytics, AI, manufacturing intelligence, observability, business applications, and real-time decision making across TSMC's global operations.

Working closely with AI Infrastructure, Cloud Platform, and Engineering teams across North America and Taiwan, you will build highly scalable, secure, and reliable data services supporting petabyte-scale data processing and mission-critical enterprise workloads.

• Define the long-term architecture and technical strategy for enterprise data infrastructure.
• Drive technical direction for scalable data services supporting global engineering and manufacturing.
• Establish architectural standards, best practices, and platform roadmaps.
• Lead architecture reviews and technical decision-making across multiple organizations.

Architect highly scalable systems including:

• Data Lakehouse
• Streaming platforms
• Batch and real-time data pipelines
• Metadata management
• Data catalog services
• Data APIs

Design highly available platforms supporting:

• Petabyte-scale storage
• Real-time streaming
• Event-driven architecture
• Data replication
• Global data synchronization
• High-throughput ingestion
• Low-latency query processing
• Multi-region deployments
• Disaster recovery
• Fault-tolerant data processing

Partner with AI platform teams to provide:

• AI-ready data pipelines
• Feature engineering infrastructure
• RAG data ingestion
• Vector databases
• AI data governance
• Data lineage
• Model data pipelines
• Training data infrastructure

• Mentor senior engineers and technical leads.
• Lead cross-functional architecture initiatives.
• Collaborate with engineering teams across North America and Taiwan.
• Influence technical direction across multiple organizations.
• Partner with business stakeholders to align platform investments with enterprise priorities.

• BS/MS/PhD in Computer Science, Computer Engineering, or related field.
• 12+ years of software engineering experience.
• 5+ years leading architecture for large-scale data platforms.
• 3+ years’ Experience as Principal Engineer, Staff Engineer, Architect, or Technical Lead.
• Strong technical communication skills for collaborating with global cross-functional teams.

Deep experience designing enterprise-scale data platforms. Hands-on experience with:

• Data Lakehouse architecture
• Data Mesh
• Data Warehousing
• Streaming platforms
• Metadata management
• Data catalog
• Data governance

Strong experience with:

• Apache Kafka
• Apache Pulsar
• Apache Flink
• Spark Streaming

Experience with:

• Apache Spark
• Apache Iceberg
• Delta Lake
• Apache Hudi
• Hadoop ecosystem
• Airflow
• Trino

Hands-on experience with:

• Relational Databases. PostgreSQL or MySQL
• No
  
  SQL Databases:
  Cassandra, MongoDB, Redis, or Elasticsearch
• Object Storage:
  Amazon S3, Azure Blob Storage, or MinIO

Strong understanding of:

• Distributed storage
• Data partitioning
• Replication
• Consistency models