Data & AI Platform Leadership for Modernization, Reliability, and Agentic Systems
I help startups and compliance-sensitive organizations rescue fragile pipelines, integrate AI-augmented engineering workflows, and design cloud data systems that support better decisions, stronger delivery confidence, and agentic automation.
The best fit is usually a focused consulting engagement with a clear business problem, an urgent trust gap, or a modernization effort that needs senior technical ownership.
Fast diagnosis for teams that need a senior read on architecture, reliability, and readiness for AI/LLM integration.
For organizations moving logic into governed platforms while integrating agentic AI to automate pipeline configuration and management.
Hands-on work to stabilize fragile ingestion, silent failures, schema drift, and alerting blind spots.
Senior leadership for 3- to 6-month stretches when you need architecture, implementation, and stakeholder alignment without a full-time executive hire.
Leadership, product, and analytics teams are spending too much time debating which number is right instead of acting on a shared view of the business.
You are outgrowing spreadsheet-heavy analytics and one-off SQL, but you want a practical roadmap and hands-on execution rather than a theoretical transformation deck.
Engineers are chasing failures, schema drift, stale data, and unclear ownership, while business users have started to lose trust in reporting.
You need traceability, validation, and stronger operating discipline for healthcare or other high-stakes data flows without slowing delivery to a crawl.
Move critical business logic from Excel, Google Sheets, and one-off SQL into governed warehouse models, orchestration, and documented operating practices.
Leverage agentic augmentation to automate dbt and BigQuery pipeline configuration, reducing manual overhead and improving management at scale.
Design event-driven, cloud-native platforms on GCP or AWS using practical patterns that balance delivery speed, maintainability, observability, and long-term cost control.
Build auditable ingestion, transformation, and delivery systems for healthcare and other high-stakes environments where traceability and validation cannot be treated as afterthoughts.
Reliable pipelines, governed transformations, and clearer ownership help teams make revenue and operating decisions with less debate.
I work hands-on across architecture, implementation, debugging, and operations, which shortens the path from diagnosis to production improvement.
Platform choices are tied to business value, cloud spend visibility, and maintainability, not abstract architectural fashion.
Integrating LLMs into engineering workflows using OpenClaw for structured output generation, pipeline diagnostics, and automated validation frameworks.
Principal quality and platform engineering for GCP. Explored LLM-assisted validation and anomaly detection to accelerate triage in regulated healthcare pipelines.
Designed and built secure AWS transfer infrastructure with Kafka, Python services, PostgreSQL, Terraform, observability, and explicit cloud cost accountability.
Senior technical leadership across clinical data processing, DICOM archive search, data anonymization analysis, modernization, and validated systems in regulated environments.
Yes. I support architecture reviews, project rescue work, modernization planning, reliability remediation, and hands-on delivery leadership for teams that need senior depth without adding a full-time executive hire.
Most projects start with an assessment or a narrowly scoped remediation effort, then expand into implementation, modernization, or technical leadership if the fit is right.
Typical stacks include Python, SQL, Airflow, dbt, BigQuery, PostgreSQL, Kafka, Cloud Functions, Cloud Run, Snowflake, AWS infrastructure, and observability tooling.
I start by locating the trust gaps, operational risks, and architectural constraints that are actually slowing the business down.
I favor phased improvements, clear ownership, and operating discipline over abstract redesigns that are expensive to adopt.
I work hands-on through implementation and stabilization, then leave behind architecture clarity, monitoring, and a team that can keep moving.
If your team is wrestling with pipeline instability, spreadsheet bottlenecks, metric trust issues, or regulated data complexity, I can help assess the problem, define a practical roadmap, and lead the implementation.
kris.kokomoor@gmail.comBest fit: startups scaling analytics, teams that need a 3- to 6-month technical lead, healthcare data organizations, and companies with high-stakes reporting or compliance requirements.