Data Pipelines & Backend Infrastructure
Production-grade pipelines that ingest, transform, and serve data reliably at scale.
I design and build end-to-end data pipelines — from raw ingestion through transformation to serving — using Python, FastAPI, PostgreSQL, and Docker. The focus is always on reliability: idempotent jobs, observable metrics, schema validation, and graceful failure handling.
Use Cases
Multi-source data aggregation
Pull data from 5+ external APIs on a schedule, normalise to a common schema, store in Postgres, and expose via a read API for dashboards.
Event-driven ETL
Webhook triggers → validate → transform → persist → notify. Used for payment events, CRM updates, form submissions.
Data warehouse pipelines
Move operational data into an analytics warehouse (BigQuery, Redshift) with incremental loads, SCD2 for history, and dbt for transformation.
Internal API backends
FastAPI + PostgreSQL backends that serve data to dashboards, mobile apps, or third-party tools — with auth, rate limiting, and OpenAPI docs.
Common Questions
How do you handle schema changes in the source data?
Pydantic models with strict validation at ingestion boundaries. When upstream schema changes, the model migration is a PR — reviewable, testable, rollbackable.
What if the pipeline fails halfway through a run?
Idempotent design means re-running is always safe. Content-hash deduplication prevents double-inserts. Checkpoint tables track progress so partial runs resume from where they stopped.
Do you use dbt?
Yes, for analytics-layer transformations where a transformation history and data lineage graph matter. For operational pipelines, Python + Pydantic is usually cleaner.
What you get
- →Ingestion pipelines (APIs, files, databases, event streams)
- →Transformation with Pydantic schema validation
- →PostgreSQL schema design and Alembic migrations
- →FastAPI endpoints for data serving
- →Orchestration with Prefect or scheduled cron jobs
- →Monitoring: row counts, latency, error rates, last-run timestamps
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