• Define and own the end-to-end data and analytics architecture strategy • Design scalable batch, streaming, and real-time data systems • Establish standards for data modeling, semantic layers, and reporting • Lead architecture reviews and technical decision-making • Drive adoption of modern architectures (lakehouse, data mesh, real-time analytics) • Design and prototype critical data platform components • Write production-quality code for complex or high-impact areas • Review schemas, transformations, dashboards, and analytics models • Troubleshoot performance and reliability issues across pipelines and queries • Optimize workloads for latency, concurrency, and cost • Design and architect a scalable data platform supporting ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines. • Design a "Data for Agents" strategy, ensuring our data warehouse is structured with the semantic layers and metadata necessary for LLMs to navigate it accurately. • Build AI-ready data infrastructure, including vector stores, embedding pipelines, and retrieval systems that power LLM and agentic workflows. • Develop a RAG-ready data architecture that enables trusted enterprise data retrieval with strong lineage, governance, security, and observability. • Create curated data products and reusable APIs that make high-quality datasets easily consumable by applications, analytics platforms, and AI agents. • Enable self-service data access for engineering, analytics, and business teams through standardized models, semantic layers, and platform capabilities. • Partner with AI, product, and engineering teams to support training datasets, feature stores, and production AI inference pipelines. • Build agentic ETL/ELT pipelines that use AI agents to autonomously discover sources and generate transformations. • Ensure reliability, scalability, and resilience of the platform, including high availability, monitoring, and disaster recovery readiness. • Partner with product, finance, business operations, and leadership teams to define analytics needs • Design scalable data models for reporting and advanced analytics • Ensure analytics solutions are performant, trustworthy, and easy to use • Drive adoption of data-driven culture through reliable insights • Define data governance, lineage, cataloging, and metadata standards • Establish data quality frameworks and validation processes • Ensure privacy, compliance, and secure access to sensitive data • Implement role-based access controls and auditability • Mentor senior engineers, analytics engineers, and data scientists • Partner with product, ML, platform, and business teams • Translate business questions into scalable data solutions • Influence roadmaps using data platform and analytics considerations • Act as the executive technical authority for data and analytics • Define SLAs/SLOs for data availability, freshness, and accuracy • Establish monitoring, alerting, and incident response processes • Optimize cloud costs and query performance • Support capacity planning for data growth • Be an evangelist for pragmatic AI adoption. • Help establish a culture of outcome-driven innovation.