Data Pipeline & ETL Engineering
Custom data pipelines that reliably process millions of records — built for correctness, observability, and production workloads.
10M+ Throughput Records processed per pipeline run
<5min Data Freshness Maximum data staleness in destination
99.9% Uptime Pipeline availability in production
THE PROBLEM
Data from multiple sources doesn't match or sync correctly
ETL jobs fail silently and nobody knows until reports are wrong
Pipeline runs take too long and block downstream analytics
Schema changes in source systems break the entire pipeline
No visibility into what was processed, when, or whether it succeeded
THE APPROACH
01
Data Source Audit
Map all data sources, schemas, volumes, and quality issues. Identify transformation requirements and delivery SLAs.
02
Pipeline Architecture
Design idempotent pipelines with schema-on-read staging, proper error handling, and monitoring. Choose batch vs. streaming based on freshness requirements.
03
Build & Test
Implement extraction, transformation, and loading with comprehensive data validation, deduplication, and quality checks at every stage.
04
Monitor & Evolve
Deploy with data freshness monitoring, alerting on failures, and automated recovery. Adapt to source schema changes without downtime.
RESULTS
✓ 10M+ records per run High-throughput processing with Go and Python
✓ <5 minute data freshness Near real-time data availability in destinations
✓ Zero silent failures Every failure triggers an alert with context for quick resolution
✓ Idempotent by design Safe to re-run any pipeline without data corruption
TECHNOLOGIES
RELATED WORK
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