01
Platforms other engineers build on
Frameworks, compilers and shared libraries that turn repeated pipeline work into configuration, released like a product.
Software Engineer & Data Platform Architect
I build data platforms as software.
Batch and streaming pipelines, lakehouses and warehouses, the storage and database layers beneath them, and the frameworks, services and infrastructure around them.
every diagram here is a sketch like this ↘︎
§ 01 — How I work
The engineering underneath
02
Data that stays correct when things fail
Replayable stages, idempotent writes, automatic recovery, and releases that switch atomically and roll back in one step.
03
From engine internals to architecture
How a query engine plans a join, how a table format commits, how a log replicates: knowing both ends keeps designs as simple as the problem allows.
§ 02 — Selected systems
Core-team work
2025 — now
Itineris · NEXUS lakehouse
Versioned ERP data in each customer's own Azure tenant, on Iceberg and Spark.
2021 — 2023
BMW · Cloud Data Hub
Pipeline frameworks used by 200+ data engineers, including a no-code compiler.
2020 — 2021
Renault · Pandora
Real-time plant and vehicle data on Beam, with replay as a first-class feature.
2019 — 2020
Daimler · road conditions
Stateful streaming that turns fleet sensor events into map data.
§ 03 — Blog
How the machinery works
Jobs, stages and tasks
How an action becomes a job, how the scheduler cuts it into stages at shuffle boundaries, and how tasks find executor cores, locality and retries included.
Storage, caching and memory
What an executor's memory holds, how execution and cached data share one pool, and why a container gets killed while its heap looks half empty.
Shuffle, partitioning and joins
How a shuffle moves rows between stages, how many partitions to use, and how Spark chooses and runs joins, with AQE splitting the skewed ones.
§ 04 — Contact
Let's talk tech.
distributed systems, architecture, engine internals