Data Pipelines
Get data from A to B, reliably. Batch, streaming, or both. Built to run without hand-holding.
Context
Pipeline engineering
A pipeline that runs is easy. A pipeline that runs correctly at 3am on a holiday, handles failures gracefully, and remains understandable six months later, that's engineering.
I build pipelines with proper testing, monitoring, and documentation. The kind that don't page you at midnight.
Capabilities
What I build
Batch Processing
Scheduled data processing for analytics, reporting, and warehouse loads. Optimized for reliability and cost efficiency.
- ETL/ELT pipeline development
- Incremental processing patterns
- Data quality and validation
- Idempotent, rerunnable jobs
Real-Time Streaming
For when batch isn't fast enough. Sub-second latency with proper handling of late data and failures.
- Spark Structured Streaming
- Kafka-based event streaming
- Change Data Capture (CDC)
- Event-driven architectures
Data Integration
Connect to any source: APIs, databases, SaaS platforms, files, message queues.
Orchestration
Airflow, Dagster, Databricks Workflows, or cloud-native schedulers.
Monitoring & Alerting
SLA tracking, anomaly detection, and runbooks for when things go wrong.
Honest Take
Do you actually need real-time?
Real-time streaming is 3-5x more complex and expensive than batch. Most "real-time requirements" are actually "faster batch" requirements.
Before building streaming infrastructure, I'll help you figure out if you actually need it, or if hourly batch would solve the problem.
Fraud detection: yes, real-time matters
Live operational dashboards: maybe, depends on use case
Analytics dashboards: probably not, hourly is usually fine
Monthly reporting: definitely not
Stack
Technologies
Need reliable data pipelines?
Let's talk about what you're trying to move and where.