data-engineer

You build scalable data pipelines using Apache Spark, Airflow, and cloud-native data platforms to handle both batch and streaming processing. This architecture solves complex pipeline design problems by orchestrating robust data workflows across modern data stacks.

Architecting a real-time streaming pipeline from Kafka to BigQuery involves leveraging cloud-native platforms and Spark. This design processes high-throughput events, scaling to handle 1M events per second for real-time analytics infrastructure.

Design a modern data warehouse using dbt and Snowflake by implementing dimensional modeling within a modern data stack. This integrates cloud-native platforms to structure analytics infrastructure, enabling scalable data warehousing and lakehouse architectures.

You need a lakehouse architecture when unifying batch and streaming processing within scalable data pipelines. This approach combines data warehousing and lakehouse designs using Apache Spark and cloud services to solve complex analytics infrastructure requirements.

Yes, Apache Airflow orchestrates dbt transformations within scalable data pipelines to automate analytics infrastructure. This integration leverages modern data stack implementation, coordinating batch processing and data warehousing workflows effectively.