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.
Hiring me for Data Pipelines
A pipeline that pulls, cleans, enriches and routes your data on its own, with validation and monitoring so bad data doesn't travel downstream.
Who it's for
- Teams pulling data from APIs or exports by hand
- Document intake that someone re-keys into another system
- Agents or reports that need clean, current data to work from
- Pipelines that fail silently or need regular babysitting
What you get
- A scheduled or event-driven pipeline
- Transformation and validation rules
- Document processing where needed
- Monitoring and failure alerts
- Documentation of sources, schedules and schemas
What I need from you
- Access to the source systems and the destination
- Sample data, including bad records
- How fresh the data needs to be
- A technical contact
How it runs
Typically 2–5 weeks per pipeline.
- Week 1
Sources and targets
What comes in, from where, and where it has to land.
- Week 1–2
Design
Schedules, transforms, validation and storage.
- Weeks 2–4
Build and test
Build against real data, including the messy cases.
- Weeks 3–5
Run and hand over
Monitored rollout and handover.
A single data pipeline starts around $8,000. Larger data platforms are quoted on scope.