Profile
Engineering leader at the intersection of data engineering, applied AI, and
technical product strategy, delivering value through team leadership, driving roadmaps, and shipping
capabilities that
produce measurable business outcomes. A progression from database and pipeline engineering through
technical
and product leadership of large-scale enterprise platforms and AI-enabled features across a global
EdTech
company. Deep background in cloud-native data architecture, distributed systems, and real-time
pipelines, with recent work
encompassing Databricks platform governance, generative AI capability development, and product
strategy.
Experience
McGraw-Hill Education · Columbus, Ohio
Engineering leader and technical authority building AI Data Enablement
from the ground up and driving applied AI initiatives across the organization, owning canonical
data standards and working closely with data science, platform engineering, and data analytics.
Hire and lead distributed engineering teams, set platform roadmap, deliver BI dashboards and
reporting for the business, and engineer the underlying Databricks platform solutions, including
ETL pipelines and graph data workflows.
- Architect and engineering lead for the AI Data Enablement program — a shared framework
enabling McGraw-Hill's AI Platform applications to produce, share, and consume consistent,
trustworthy data with minimal custom work, giving leadership a unified view for investment
and portfolio decisions across the enterprise — owning the canonical event schema end to
end: designing the schema, data contract framework, and SDK ecosystem; coordinating
implementation with application teams; and partnering with Data Analytics leadership to
ingest event and Pendo data into the datalake and surface adoption metrics through Tableau.
- Managed multiple teams of individual contributors, including offshore and nearshore
contractors and distributed FTEs, across concurrent projects — owning full-cycle hiring,
setting priorities, reviewing deliverables, and maintaining alignment with executive
priorities.
- Managed AI platform roadmap and feature prioritization — translating business objectives
into engineering outcomes across multiple product lines and aligning delivery with
executive priorities.
- Spanned the full spectrum of applied AI delivery — hands-on prototyping of LLM
integrations, RAG pipelines, and agentic workflows; data engineering support for data
science model development; and team/roadmap leadership shipping AI features to production
— bringing both technical depth and delivery accountability to AI initiatives across the
organization.
- Built BI dashboards to meet stakeholder requirements, surfacing usage and cost metrics and
product-specific KPIs, providing the data foundation for roadmap decisions and executive
reporting.
- Engineered enterprise data platform solutions on Databricks including medallion
architecture, ETL pipelines in Scala and Python, compute governance, and graph data
integration workflows supporting AI features and educational outcomes analysis.
Stack AWS · Azure · GCP · Databricks · Bedrock ·
Foundry · Python ·
Scala · SQL · JSON Schema · Neo4j
McGraw-Hill Education · Columbus, Ohio
Technical leadership for cloud-native platform engineering supporting
large-scale digital education infrastructure — managing database engineers, driving architecture
decisions, and owning delivery practices across distributed teams.
- Led a team of database engineers directing architecture decisions and delivery practices
across distributed cloud-native platform initiatives.
- Directed major database migrations to Amazon Aurora, improving scalability, performance, and
reliability for high-traffic production systems serving millions of learners.
- Built CI/CD pipelines using Turbot, Terraform, and CircleCI, standardizing secure and
consistent deployments across engineering teams and reducing deployment risk.
- Strengthened platform observability and proactive performance management using CloudWatch,
Sumo Logic, and New Relic — reducing incident response time and improving production
reliability.
- Contributed to real-time data pipeline development and internal tooling in Java, Scala, and
Node.js to enhance platform extensibility.
- Directed RDS migrations and developed training and documentation supporting platform
adoption across distributed teams.
Stack AWS · GCP · Aurora · Databricks · OpenSearch ·
Terraform
Halo Communications · Columbus, Ohio
Led modernization of a mission-critical clinical communication platform
under HIPAA and HITRUST compliance requirements — defining and executing a strategic roadmap
that delivered significant reliability and performance improvements while platform usage
tripled.
- Achieved up to 72% improvement in response times while platform usage increased 252% —
through targeted optimization strategy and cross-functional execution with DevOps and
application teams.
- Engineered a high-availability SQL Server architecture using AlwaysOn Availability Groups
and Windows Server Failover Clustering across a three-node enterprise cluster.
- Defined and executed a strategic roadmap balancing platform stabilization with long-term
scalability objectives, working directly with executive stakeholders.
Stack AWS · SQL Server · AlwaysOn Availability Groups
· WSFC
McGraw-Hill Education · Columbus, Ohio
Led architecture and scaling of data infrastructure for a global digital
education platform serving millions of users, directing a team of database engineers through
sustained high-growth.
- Technical lead for a team of 4 database engineers, directing architecture decisions and
scaling strategy for a
platform supporting 20K+ concurrent users.
- Scaled the core Oracle RAC application database from 250GB to 2TB on a four-node cluster,
supporting 20K+ concurrent users and 22M+ monthly sessions — directly enabling product
growth without platform constraint.
- Built a real-time clickstream analytics pipeline using AWS API Gateway, Lambda, Kinesis, and
Elasticsearch — an early
production streaming implementation on AWS-native architecture delivering visibility into
EdTech usage metrics and user
behavior.
- Designed scalable data models, caching strategies, and content structures to support a
high-volume, user-facing digital platform.
Stack AWS · Oracle RAC · Kinesis · API Gateway ·
Elasticsearch · Java · SQL · PL/SQL