Chris Shaffer
CTO / Data Architect
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I have a long track record of successfully building software products from the ground-up, raising funds, going to market, and turning around troubled teams. I like to use hands-on work and delivering working code as a vehicle to derive more strategic and impactful insights. There is no "non-technical CTO" here.
I have over a decade of experience building technology. That experience ranges from teams in the hundreds to just myself, from pre-series A startups to companies older than my grandparents. I’ve built software and teams for startups – repeatedly – that transformed the definition of “technology” in the office’s vernacular from “that thing that’s always broken” to “the competitive advantage that pushes us forward when all else fails”.
After graduating with a Bachelor's and Master's in Computer Science from Stevens Institute of Technology, Chris went on to develop his career in application architecture, software development, and data engineering. He's worked on financial, business, healthcare, biotech, and consumer platforms as an independent consultant with numerous successful client projects, as Chief Technology Officer at Climb Credit, as Director of Engineering at Relationship Science, and at Capital IQ.
Essential but Overlooked Data Management Principles
Workshop covering a gamut of topics for how to architect and manage a world-class database providing a source of truth. Topics:
Maintaining Meaningful Determinism - Data Ingestion & Flows, Transaction Models, Identifying Entities
Data Lineage - History, Audits, Change Tracking, Sourcing
Migrations and Testing - Schema & Data Consistency, Zero Downtime
Governance - Creating Accurate Metadata, Automated and Human Data Enrichment, Entity Management and De-duplication, Access Control
Caches & Specialized Databases - Synchronization Strategies, Avoiding the “Two Wristwatches” Problem
An Introduction to Practical Automation
Matching Software Tools to Business Processes
A presentation and discussion covering the basics of automating business tasks, from simple to complex. Gives a starting point and methodologies to incorporate advanced machine-learning, traditional rules-based automation, or just simple process reform - and guidelines for which one(s) to use when. Includes real-world examples based on successful projects.
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