• Platform Architecture: Define and own the enterprise data platform architecture across cloud infrastructure, ingestion, storage, transformation, warehousing, and ML/AI/CitDev enablement layers.
• Cloud Infrastructure: Design scalable cloud-native infrastructure on Azure, AWS, or GCP, including managed compute, networking, storage, and orchestration to support diverse data workloads from batch ETL to real-time streaming.
• Data Warehousing & Analytics: Architect the enterprise data warehouse and lakehouse strategy, establishing standards for data modeling, semantic layers, and analytics delivery across business domains.
• ML/AI Platform Enablement: Build the technical foundation for ML/AI workloads: feature stores, ML pipelines, model serving infrastructure, and data contracts that bridge data engineering and data science.
• Agentic AI Frameworks: Define standards for how AI agents interact with Kiewit's data assets, establishing guardrails around data access, auditability, and output validation in agentic workflows.
• Citizen Development: Define guardrails, templates, and platform standards that empower citizen developers while maintaining data quality, security, and consistency across the enterprise.
• Governance & Quality: Establish platform governance including data quality frameworks, lineage tracking, metadata management, access controls, and compliance with enterprise security standards.
• Technology Strategy: Evaluate and guide adoption of modern data platform technologies (e.g., Databricks, Snowflake, dbt, Apache Iceberg, Kafka, Dagster,etc.) aligned to construction-industry needs.
• Technical Leadership: Provide architectural mentorship to senior and mid-level data engineers; influence cross-functional teams and shape engineering culture across the data organization.
• Stakeholder Partnership: Partner with project technology leads and business stakeholders to translate operational challenges into platform capabilities and communicate architectural decisions clearly to executive audiences.
