New York, NY · brycew.hello@gmail.com
Analytics engineer with 7+ years building canonical data models, semantic-layer infrastructure, and AI-native analytics workflows across healthcare and fintech startups. Currently leading billing data architecture and a company-wide semantic layer migration at Pelago Health. Excited to apply this foundation to building trustworthy, scalable analytics infrastructure for AI products.
New York, NY · Reports directly to CFO · Virtual specialty substance use clinic serving employers and health plans.
Re-architected Pelago’s billing data platform from a monolithic dbt pipeline into a modular, snapshotted intermediate-model system with layered dbt tests and lineage documentation — powering enterprise billing workflows across 10+ clients. Partnered with data engineering to orchestrate upstream ingestion through Airflow DAGs, establishing canonical billing datasets serving Finance, Operations, Product, and GTM reporting.
Led company-wide migration strategy from Looker to Cube semantic-layer architecture to standardize metric definitions, enable governed self-serve analytics across 14 business domains, and ground LLM-assisted querying workflows in a trusted metric layer.
Built and shipped an AI-assisted analytics QA and incident-routing pipeline integrating dbt, Elementary, Slack, Claude, and Jira. The system detects billing data anomalies, generates LLM-grounded failure summaries from dbt manifest and model lineage, and auto-routes triaged tickets to the right owner — replacing manual on-call inspection and significantly reducing time-to-triage on billing incidents.
Partnered with GTM teams (Sales, Customer Success, Marketing) to define and instrument funnel, account-health, and member-engagement metrics; owned the Member Strategy Dashboard end-to-end and contributed to the multi-phase Member Journey Dashboard used by executive leadership to monitor member activation, care initiation, treatment engagement, and retention.
Built within-member engagement anomaly detection models using rolling z-score baselines against behavioral signals, translating qualitative clinical-operations heuristics into scalable, data-driven prioritization workflows.
New York, NY · B2C fintech offering embedded card products and dental benefits.
Data Scientist (Part-time Contract) · May 2024 – December 2024
Data Analyst, promoted to Data Scientist · February 2021 – January 2024
Built and maintained production dbt pipelines transforming nested transactional and protobuf-based payments data into documented canonical models for product analytics, risk monitoring, operational reporting, and experimentation.
Developed reusable Jinja macros to convert protobuf-derived JSON blobs into structured BigQuery data models used across multiple analytics domains — reducing model development time and enforcing consistent data definitions.
Led enterprise BI migration from Periscope to Looker/LookML, designing reusable semantic definitions and enabling scalable self-serve analytics across Product, Engineering, Operations, and non-technical business teams; ran enablement sessions and office hours to accelerate adoption.
Defined success metrics for Product and Engineering roadmap initiatives, identifying key levers for increasing card utilization and reducing operational burden; partnered with stakeholders to translate data requirements into actionable insights.
Implemented custom Python ETLs to extract third-party API data for exploratory analysis in Jupyter Notebooks, including developing and evaluating risk rules using statistical performance metrics on fraud-related features.
New York, NY
Owned 30+ end-to-end analyses evaluating life-sciences marketing impact on healthcare behaviors using privacy-safe claims, EHR, and prescription-drug datasets — delivering actionable, scalable campaign recommendations to pharmaceutical clients.
Identified operational inefficiencies and designed troubleshooting workflows and tooling improvements as part of an internal operational analytics working group.
Ingested, explored, and packaged COVID-19 marketing-trend data for client workshops and industry-facing content.
New York, NY
Built web-scraping pipelines and performed quantitative and qualitative analysis in R (tidyverse, ggplot, sentiment analysis) on 500+ social media comments to evaluate engagement for a HRSA-funded HIV intervention; instrumented Google Analytics on the program website.
Developed and led presentations on new tools and technologies for digital health program promotion.
New York, NY
Coded and analyzed point-of-sale advertising survey data for tobacco products across New York City retailers.
Contributed to research design, survey instruments, and policy reviews for a study on social media advertising of cannabis vaporizer products; supported manuscript development for publication.
Spillane, T.E., Wong, B.A., Giovenco, D.P. (2020). Content analysis of Instagram posts by leading cannabis vaporizer brands. Drug and Alcohol Dependence. https://doi.org/10.1016/j.drugalcdep.2020.108353
Giovenco, D.P., Spillane, T.E., Wong, B.A., Wackowski, O.A. (2019). Characteristics of storefront tobacco advertisements and differences by product type. Preventive Medicine, 123, 204–207.