3. Data Pipelines & Orchestration

I design and implement production-grade data pipelines that transform raw data into analytics-ready datasets — with robust orchestration, testing, and observability built in.

Transformation with dbt

Build modular, testable, and documented transformation layers:

• dbt Core / dbt Cloud — SQL-based transformations with version control, testing frameworks, and automated documentation

• Incremental models — Efficient processing of large datasets with incremental materialization strategies

• Cross-environment patterns — Development, staging, and production environments with proper isolation and promotion workflows

Orchestration Platforms

Reliable scheduling and dependency management for your data workflows:

• Apache Airflow — Industry-standard DAG-based orchestration with extensive operator ecosystem and custom integrations

• Prefect — Modern Python-native orchestration with dynamic workflows, automatic retries, and cloud-native deployment

• Dagster — Software-defined assets with built-in data quality checks, lineage, and observability

Key Capabilities

Pipeline Architecture	Medallion patterns (bronze/silver/gold), dependency graphs, and modular design
Data Quality	Schema validation, freshness checks, and automated testing with dbt tests
Backfilling & Recovery	Idempotent pipelines, historical reprocessing, and failure recovery
Observability	Pipeline monitoring, alerting, SLA tracking, and cost attribution

Pipeline Patterns

• ELT workflows — Extract-Load-Transform patterns optimized for cloud warehouses

• Data contracts — Schema enforcement and producer-consumer agreements

• Data sensors — Event-driven triggering based on data availability

Explore related topics:

• dbt Incremental Models

• Schedule with Airflow

• Data Sensors

• Data Backfilling