Raw data sitting in disconnected silos is not a data platform — it is a liability. Building systems that ingest, transform, reconcile, version, & serve data reliably at enterprise scale is what separates engineers who prototype from architects who build infrastructure teams depend on. This program teaches you how to do the latter.
Pipeline Architects is an intermediate program designed for data engineers, analytics engineers, & data platform professionals who want to build complete, production-ready data engineering skills. Across ten focused courses, you will master the full data engineering stack: mapping data flows, ingesting from relational databases, streaming platforms & REST APIs, building and transforming modular pipelines, evaluating storage formats, loading warehouses incrementally, implementing SCD2 historical tracking, applying data lake transactions and versioning, building lakehouse architectures, automating workflows with Apache Airflow, and unifying data through SQL MERGE reconciliation and performance tuning.
You'll work with industry-standard tools including Python, SQL, Apache Airflow, dbt, Snowflake, Apache Kafka, Airbyte, Delta Lake, Iceberg, and Hudi, applying hands-on techniques to realistic production data engineering scenarios.
By the end of the program, you will be equipped to architect, build, & operate data pipelines from raw ingestion through lakehouse delivery with the reliability and performance that modern analytics infrastructure demands.
Projet d'apprentissage appliqué
Throughout this program, you will complete hands-on projects that reflect real data engineering workflows. You will design end-to-end data flow diagrams, configure Airbyte connectors for relational databases, Kafka topics, and REST APIs, and build modular pipeline stages for ingestion, cleansing, transformation, and loading using Python, dbt, and Airflow. You will benchmark columnar and row-oriented storage formats, implement incremental warehouse loading using Snowflake MERGE INTO, and apply SCD2 logic to build historical dimension models. You will convert raw files to transactional formats, execute time-travel queries, manage schema evolution, and register external tables across Delta Lake, Iceberg, and Hudi. You will configure production-grade Airflow DAGs with retry logic, SLA alerting, and Slack integration, and apply SQL MERGE upsert operations with field-level conflict resolution and performance tuning. Each project produces a defensible, production-applicable artifact.
