Tim Frazer

Estate-planning platform — 1M+ users, 20,000+ advisors, 200+ enterprise partners incl. AARP, Fifth Third, LPL Financial, UBS & USAA. EstateOS™ launched 2025 · CNBC Disruptor 50, Inc. 5000.

• Run a lean data team — data engineer, analytics engineers, analysts, and a data PM on Bauplan, Snowflake, dbt, and Estuary.
• Leading migration to an Apache Iceberg lakehouse (Bauplan) — the first AI-first data architecture of its kind at the company, with source-level ownership and reproducible data over time, and root-cause analysis as the operating model.
• Designed RBAC and data-masking policies for PII-sensitive estate-planning data, partnering on the compliance program.
• Ran a data & AI maturity assessment on my own team found we were at Level 1 despite strong tooling (Cursor, Claude, Snowflake Cortex, Bauplan MCP). The gap was missing specification artifacts and data contracts, not tools.
• Built a Data Value scoring framework and an Information Product Canvas with product owners to give the team a consistent way to prioritize.

• Molecular You — designed and built a HIPAA-compliant data lake from scratch (Snowflake, dbt, AWS, CI/CD). Two people, a tight budget, weeks not months. Built data masking and governance controls for sensitive health data in a scientific context.
• Vanguard Charitable (Lead Data Architect · 2024–25) — documented and redesigned dataset architecture across multiple stakeholder domains and delivered a working AWS data-lake proof-of-concept showing what good data modeling looks like and doing real analytics could do.
• Trust & Will (pre-hire) — gap analysis, team topology, and technical roadmap in three months. The engagement turned into the director role.

Wellness platform — led data engineering, ML, and analytics across GCP, AWS, and Azure.

• Grew the data engineering and ML team from 0 to 12 — senior engineers, analysts, and ML — and shipped production ML features to customers within the first 90 days.
• Owned the full GCP data platform (BigQuery, dbt, Terraform, Dagster, Python) and set architecture and delivery standards for reliability, scalability, and ML-feature readiness.
• Built a data governance and security strategy that lifted data quality to where the ML team could actually use it — not the state when I joined.

First data engineering hire at the company.

• Scaled from 0 to 3 engineers while managing GCP, AWS, and Azure infrastructure as the user base grew from thousands to tens of thousands.
• Built the first data-warehouse ingestion process and the data engineering foundation the rest of the platform was built on.

E-commerce — platform, pipelines, and data science infrastructure.

• Migrated from Redshift/Luigi to Snowflake/Airflow — improved query performance and lowered ingestion costs using Snowpipes and AWS Lambda with Pydantic validation.
• Built and managed a Kafka cluster for near-real-time CDC from the primary e-commerce platform.
• Developed the AWS EMR and SageMaker deployment stack for the data science team.
• Overhauled the dbt modeling process — testing, staging layers, documentation — and data reliability noticeably improved.

• Python, PostgreSQL, and API development for e-commerce systems. Owned data architecture and the messaging queue for the warehouse interface.

• Built NetSuite applications for multi-warehouse inventory — cut order entry from days to minutes through warehouse integrations and scripting. Led development across multiple contractors.

• Built KPI frameworks across finance, sales, and operations. 50% increase in order-processing speed through NetSuite automation and warehouse-connector work.