Understand client requirements and lead the creation of robust data platforms leveraging Data Lakehouse architectures, serving as the primary subject matter expert for the Azure data ecosystem (Microsoft Fabric, Azure SQL, and Power BI).
Design and implement scalable data models, ETL/ELT pipelines, and analytics layers using modern cloud ecosystems.
Architect Microsoft Fabric reporting solutions, establishing medallion layering, managing capacity planning, ensuring freshness governance, and configuring CDC/mirroring from Azure SQL into Fabric.
Enforce strict data-layer segregation ensuring PII is excluded by construction, maintaining multi-tenant isolation within the analytics layer, and strictly adhering to 10+ year regulated data retention policies.
Lead complex migration efforts off legacy systems (e.g., AirTable, Smartsheet) to Microsoft Fabric, effectively managing mid-program form and rule drift, and establishing reliable, per-client export channels.
Lead end-to-end analytics projects — from requirement gathering to deployment.
Mentor junior team members, perform code reviews, and ensure adherence to quality standards.
Work closely with cross-functional teams (engineering, product, business) to align data strategy with business goals.
Develop and optimize complex SQL queries, Power BI semantic models, dashboards, and reports (transitioning from legacy LookML/BigQuery paradigms).
Troubleshoot performance bottlenecks, data inconsistencies, and integration challenges.
Stay up to date with advancements in the analytics and cloud data ecosystem.
Drive innovation in reporting and analytics capabilities.
Recommend and implement new tools, frameworks, and practices to enhance the data platform.
Define best practices, coding standards, and governance for analytics projects.
Monitor data systems performance and implement optimization strategies.
Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.
Build and oversee AI-ready data pipelines, focused on feeding scrubbed, segmented data to assistive AI (embeddings, vector stores, and feature stores) and integrating GenAI into analytics and data products.
Drive adoption of AI-assisted data engineering across the team: copilots for SQL/transformation/pipeline code, plus automated testing and documentation.
Define governance and quality standards for data consumed by LLMs and agents.
