Data Architect

End-to-end architecture for a Unified Data Platform spanning manufacturing, enterprise, and analytics domains

Define reference architectures for data ingestion, storage, processing, analytics, and AI enablement

Drive convergence of structured, semi-structured, unstructured, telemetry, and time-series data into a cohesive platform across data technologies

Establish clear platform patterns (lakehouse, streaming-first, event-driven, domain-oriented)

Familiarity with semiconductor manufacturing data landscapes (fab, test, assembly, packaging)

Ability to bridge OT and IT data architectures in regulated manufacturing environments

Design and governance of lakehouse architectures

Strong experience with:

• Data lake zoning (raw, curated, trusted, feature layers)
• Warehouse and analytics integration
• Semantic and consumption layers
• Define standards for data modeling, partitioning, schema evolution, and performance optimization
• Architect for multi-consumer access (BI, data science, ML, operations)

Hands-on architectural experience with cloud-native data platforms, Databricks, Azure, and GCP

Architecture-level knowledge of:

• Object storage
• Cloud data warehouses
• Streaming and messaging platforms

Design for resilience, scalability, and cost optimization (Fin Ops-aligned)

Architect pipelines for manufacturing telemetry

Streaming analytics

Enable operational analytics and alerting without compromising analytical workloads

Architect data foundations for AI-driven use cases

Define feature stores, training datasets, and inference data paths

Experience with graph-oriented or knowledge-based architectures for relationship-driven analytics

Ensure data architecture supports model lifecycle management and reusability.

Architect integration patterns across data workloads

Enable correlation of business, manufacturing, and operational data into unified analytical views

Define enterprise data governance frameworks aligned to platform architecture

Architect solutions for:

• Metadata management
• Data lineage and traceability
• Master and reference data strategies

Familiarity with compliance considerations relevant to semiconductor manufacturing and IP protection

Root cause analysis

Platform reliability

Enable clear ownership boundaries between platform, domain, and consumption teams

Serve as a technical bridge between:

• Manufacturing engineering
• Data science / AI teams
• Enterprise IT and security

Mentor engineers and guide implementation without becoming a delivery bottleneck

Comfortable operating in matrixed, globally distributed organizations with onshore leadership presence