Data engineering
Design and deliver Scala and Python pipelines, Spark jobs, Databricks workloads, Delta Lake models, and dependable ingestion flows.
Data engineering & applied AI / Open to select engagements
Data systems
built to work.
I build dependable data platforms, modernize backend systems, and turn promising AI ideas into tools teams can use.
Architecture
Hands-on engineering
Technical leadership
SCALAPYTHONSPARKDATABRICKSDELTA LAKEAWSDYNAMODBAIRFLOWKUBERNETESGITLAB CI/CDAPPLIED AI
01 / How I can help
Useful systems.
Thoughtfully built.
I enjoy taking ambiguous technical problems from first sketch to reliable production software—and helping the people around the work move with confidence.
Cloud & platform engineering
Connect systems across AWS and build the CI/CD, orchestration, and operations practices that keep data products dependable.
Practical AI
Prototype AI-assisted workflows, connect models to useful tools, and build in the auth, cost visibility, and failure handling they need.
02 / Outcomes
Engineering measured
in what changed.
Selected results from systems I helped build and improve.
97%
Lower real-time data latency
Simplified continuous processing to make fresh data available much sooner.
10×+
Faster batch processing
Tuned a large Spark workload from hours of processing down to minutes.
99.99%
Service availability
Improved reliability for a business-critical service through re-architecture and operational ownership.
<50 ms
Low latency at scale
Delivered responsive customer-facing services built to handle production demand.
03 / Selected work
A few kinds of
problems I like.
From customer-facing services to data platforms and AI experiments, my work tends to live where scale, reliability, and real-world usefulness meet.
CASE / 01
REAL-TIME DATA PIPELINES
Making fresh data available to downstream teams
Simplified a fragmented real-time processing workflow into a continuous-processing pipeline powered by Spark. The new design reduced end-to-end latency by 97%, lowered operational overhead and compute costs, and made fresher data practical for downstream personalization and analytics.
CASE / 02
DATA PLATFORMS
Building reliable data platforms at scale
Modernized ingestion and large-scale batch analytics, evolving older distributed-data workloads toward a governed lakehouse. Recent work spans Scala, Python, Spark, Databricks, Delta Lake, and Unity Catalog.
CASE / 03
CLOUD & PRODUCT ENGINEERING
Modernizing high-impact backend services
Re-architected business-critical services, modernized legacy APIs, and contributed to high-throughput customer-facing systems built with AWS services.
CASE / 04
APPLIED AI
Building useful AI-assisted workflows
Worked on conversational AI and agent workflows, with attention to context handling, authentication, timeouts, error visibility, and usage costs.
CASE / 05
DEVOPS & DEVELOPER ENABLEMENT
Creating dependable delivery and operations paths
Built reusable CI/CD practices and platform tooling across GitLab CI/CD, Kubernetes, Helm, and Airflow—helping teams deploy, orchestrate, and operate data products with confidence.
04 / About
I like the hard middle—
where ideas become systems.
My career has moved from backend and integration engineering into large-scale data systems, cloud architecture, DevOps, and AI-enabled tools. I still like being close to the code, and I care about the team and operational details that make a solution hold up after launch.
I’ve led small engineering teams, mentored developers, and helped shape shared tools and standards. My current work at Edmunds focuses on data engineering and practical AI.
Since 2007Professional software engineering
Up to 6+Engineers led concurrently
End to endArchitecture through operations
05 / A good place to start
Have a project
worth untangling?
I’m open to select side projects, consulting, and technical advisory work that fits around my current commitments.
Tell me about it
A short note about the problem is perfect.