Data Engineering
Schemas drift the moment a source system updates, pipelines fail in ways nobody catches until the report is already wrong, and the LLM wired into the data path hallucinates classifications downstream teams can't trust. AlgoCoder builds data infrastructure that holds under real production volume.
← All Case StudiesFeatured Data Engineering work.
Clust — GPU Cloud Platform
Most data platforms work in dev. Real production volume hits, the schemas drift, the LLMs you wired into the pipeline start hallucinating, and the cost curve goes vertical.
Production engineering for Clust — GPU cloud platform
Defense / Government Procurement Infrastructure
Build it wrong and you don't get a second chance.
Defense / government supply chain procurement
Data Engineering — recurring patterns.
ETL Pipeline Rebuild for a Reporting Layer That Was Always Stale
The overnight job ran for six hours. The data was stale by lunch.
Schema Validation Layer for a Platform With Recurring Production Breaks
Source systems were changing schemas without telling the data team. The downstream pipeline broke once a week.
Real-Time Streaming Pipeline for a Platform Whose "Real-Time" Wasn't Real-Time
The product promised real-time. The pipeline delivered ninety seconds late.
Lakehouse Migration for an Organization Whose Data Lake Had Become a Data Swamp
Years of files, no governance, no schema registry, no idea what was where.
LLM-Embedded Pipeline for Production Data Enrichment
The team had a working LLM enrichment in development. Putting it in the production data path was a different problem.
Data Quality and Lineage Implementation for a Reporting Layer Nobody Trusted
The dashboards showed numbers. Nobody trusted the numbers. Decisions were being made by gut.
Warehouse Architecture Decision for a Team Outgrowing Snowflake's Cost Profile
Snowflake had been the right choice. The team's query patterns had moved into a category where it wasn't anymore.
Change Data Capture Implementation for a Source System That Had Resisted It
The team had been doing nightly snapshots for years. Real-time downstream needs made that untenable.
Data Mesh Decomposition for a Centralized Data Team That Had Become a Bottleneck
Every product team needed data work. The central data team couldn't service the queue.
Streaming Analytics Layer for a Product That Needed to Surface User Behavior in Near Real Time
Product analytics was running on a daily batch. Product decisions needed to happen faster.
Data Warehouse Migration for a Team Stuck on a Legacy Platform
The legacy warehouse worked. It also cost more than the rest of the data stack combined and the vendor was slow-rolling features the team needed.
Data Catalog Implementation for an Organization Where Nobody Could Find Anything
The data existed. Finding it required asking three people who had been there long enough to remember.
Have a Data Engineering problem that isn't on this page?
A 30-minute call with a senior engineer. No deck. No pitch. Questions about your stack, your stage, and the bottleneck you came here to remove.