High-Stakes Reporting & Compliance
Organizations in healthcare, finance, or aerospace that require absolute traceability, rigorous validation, and audit-ready data flows.
Bridging Silicon, Cloud, and Agentic AI for Regulated Industries
I help organizations with rigorous compliance and reporting requirements build resilient data platforms. Whether it's sensor data from the edge or complex cloud-native warehouses, I bridge the gap between embedded systems, analytics modernization, and AI-augmented workflows to ensure your data is auditable, automated, and above all, trustworthy.
I specialize in environments where "close enough" isn't an option. My work is most impactful for:
Organizations in healthcare, finance, or aerospace that require absolute traceability, rigorous validation, and audit-ready data flows.
Teams outgrowing spreadsheet-heavy reporting who need a practical, hands-on lead to build a governed analytics foundation.
Companies bridging the gap between hardware (ESP32/Embedded) and the cloud, requiring unified engineering across the entire data lifecycle.
Teams looking to integrate agentic AI not as a gimmick, but as a robust operational layer for automated pipeline management and diagnostics.
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.
I support a range of needs including project-based consulting, fractional leadership (Head of Data/Platform), contract-to-hire engagements, and permanent leadership roles for organizations looking for long-term ownership.
Most projects start with an assessment or a narrowly scoped remediation effort, then expand into implementation, fractional leadership, or permanent placement 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.