Senior Solutions Architect – Redshift Specialist · AWS Current
Help AWS customers design, migrate and tune their analytics workloads on Amazon Redshift.
The distribution
Sum of 2 dice · observed vs. exactObserved shareExact probability
Senior Solutions Architect · Redshift · AWS
Slicing and dicing
data since 2007.
I'm Patrick Muller, a Senior Solutions Architect at AWS specializing in Amazon Redshift. I help customers design and scale their analytics, drawing on years of building data pipelines with Glue, Athena, Spark and EMR. Lately I've been building in public with AI agents.
Below is Data Studies / No. 001, a small physics experiment you can play with. It measures itself as it runs.
Data studies / No. 001Fair dice?
WebGPU · StartingDrag a die to pick it up. Let go to throw it.
Space rolls. Only rolls count.
Last roll
—
Ready
WebGPU unavailable
This experiment draws the dice with native WebGPU, and this browser can't provide it. Recent Chrome, Edge and Safari can.
The physics and statistics still work without the 3D view: press Run 1,000 rolls and the same engine rolls the dice off-screen.
The statistics
- Counted rolls
- 0
- Sample mean
- —
- Chi-square
- —
- p-value
- —
- Kinetic energy
- 0.00
Roll the dice to start the sample.
Throws by hand are shown but not counted, because a die can be placed rather than thrown. Energy is in illustrative units: each die has mass 1 and an edge of 0.9.
Inside the experiment
Physics. Each die is a rounded cube with mass, rotational inertia, friction and bounce. A small solver I wrote for this page (no physics library) resolves contacts with the floor, the walls and the other dice 240 times a second.
Rendering. Native WebGPU with WGSL shaders. The pips are drawn in the shader as shallow dimples, a 2048 × 2048 shadow map gives the soft shadows, and reflections come from a simulated studio with softbox lights.
Statistics. Every counted roll updates the histogram. A chi-square goodness-of-fit test compares it with the exact distribution for the number of dice, pooling rare outcomes so each bin expects at least five rolls. Run 1,000 rolls uses the same engine in a background thread.
The numbers
A career in four figures
In production since
2007First role: System Analyst, GVTAt AWS
8 yrsSupport → Specialist → Engineer → ArchitectCountries
3Brazil · South Africa · United StatesPublications & talks
5AWS Big Data Blog · Summit · YouTubeThe path
Where I've worked
RoleWhereWhen
United States2026 — now
Senior Data Engineer · Amazon
Built and ran data pipelines in Python, AWS Glue, Amazon Athena and S3 that processed terabytes from many sources, with data quality checks and validation built in.
Big Data Specialist · Amazon
Advised customers on big data architectures such as Data Mesh, Lake House and Kappa, built on Kafka, Kinesis, EMR, Glue, Athena and Redshift.
Big Data Analyst · AWS Premium Support
Production troubleshooting and performance tuning across EMR, Glue, DynamoDB, Elasticsearch, Kinesis, Kafka, Athena and Presto. Became an Athena subject-matter expert.
Solutions Architect · Grupo Mult
Ran GCP and AWS environments, with automation and monitoring for Kafka, Docker, Hadoop, Spark and Jenkins.
Middleware Specialist · Service IT
WebLogic and Java on Oracle Exalogic and SuperCluster, plus big data consulting with Hadoop, MongoDB, Splunk and Spark.
Middleware Administrator & System Analyst · GVT, TIM Brasil
WebLogic, Apache, Tomcat and JBoss infrastructure, early Hadoop (MapReduce, Hive, HBase), and billing and CRM systems on Oracle, PL/SQL, HP-UX and Solaris.
The work
Writing & talks
Process and analyze highly nested and large XML files using AWS Glue and Amazon Athena
Get started managing partitions for Amazon S3 tables backed by the AWS Glue Data Catalog
Aprimore seus aplicativos com Embedded Analytics no Amazon QuickSight
Talk in Portuguese on embedded analytics with Amazon QuickSight.
Monitoring Amazon Athena queries using Amazon CloudWatch
Speed up Amazon Athena queries using partition projection
An AI-generated commercial, built end to end with Claude Opus 5.5 in one hour
Pinned on X. Part of an ongoing build-in-public series.
Side projects
Things I'm building
The stack
Tools I reach for
Data & analytics
- AWS Glue
- Amazon Athena
- Presto / Trino
- Amazon EMR
- Apache Spark
- Hadoop
- Kafka & Kinesis
- Amazon Redshift
Languages & stores
- Python
- SQL
- PL/SQL
- DynamoDB
- MongoDB
- Oracle
- Linux
AI & architecture
- Amazon Bedrock
- Foundation models
- AI agents
- Data quality & validation
- Lake House
- Data Mesh
- Kappa
Credentials
Certifications & education
AWS Certified AI Practitioner
Early adopter.
AWS Data Lab Participant
Amazon Athena Subject Matter Expert
AWS Certified Data Analytics – Specialty
AWS Certified Solutions Architect – Associate
Graduate course, Big Data
Science, Mathematics & Computer Science
The person behind the pipelines
Engineer by trade.
Maker by habit.
I'm Brazilian, I work in English and Portuguese, and I've done this job in Curitiba, Cape Town and the United States. Beyond the technical work I mentor teammates, run interviews, and turn team metrics into something stakeholders can act on. I also share what I'm learning about AI as I go.
Say hello