Data Pipelines
I build automated data flows that ingest, clean, enrich, and route information to where it needs to go. Whether you need to pull data from APIs on a schedule, process incoming documents, or stream events between systems in real time, I design pipelines that run reliably without manual intervention.
Data is the foundation of agentic AI
AI agents are only as good as the data they can access. Pipelines ensure that data is fresh, clean, and in the right place.
Most AI projects don't fail because of the model — they fail because the data isn't there, isn't clean, or isn't structured properly. A data pipeline solves this by automating the entire journey from raw source to agent-ready format. I build these pipelines as standalone systems or as part of larger agentic architectures, depending on what you need.
The pipelines I build are designed for reliability first. That means proper error handling, retry logic, data validation at every stage, and monitoring so you know immediately when something goes wrong rather than discovering stale data weeks later.
What I build
End-to-end data flows tailored to your sources, formats, and destinations.
API polling and ingestion
Scheduled jobs that pull data from external APIs, SaaS platforms, and web services. I handle pagination, rate limiting, authentication, and incremental fetching so you only process what's new.
Document processing
Pipelines that extract structured data from PDFs, spreadsheets, emails, and other document formats. Includes OCR, parsing, classification, and transformation into formats your systems can work with.
Real-time event streams
Event-driven architectures that react to changes as they happen — webhook receivers, message queue consumers, and streaming processors for use cases where batch processing is too slow.
ETL and data transformation
Extract, transform, and load processes that reshape data from source formats into the structures your agents, databases, and reporting tools expect. Schema mapping, type conversion, deduplication, and enrichment.
Data validation and quality
Validation checks at every pipeline stage to catch malformed data, missing fields, schema violations, and anomalies before they propagate downstream. Bad data is quarantined and flagged, not silently passed through.
Scheduling and monitoring
Cron-based and event-triggered scheduling with full observability. Dashboards for pipeline health, alerting on failures or data quality issues, and logging for debugging and audit trails.
Need to get your data flowing?
Tell me about your data sources and where the data needs to go. I'll design a pipeline that runs itself.