senior-data-engineer

Batch ETL pipeline design uses Airflow for orchestration and Spark for distributed processing. You define YAML configurations to generate DAGs, automate execution, and apply data quality validation across scalable workflows.

Streaming data quality monitoring validates live records within Kafka and Flink architectures. It applies DataOps controls to detect anomalies, enforce governance, and maintain reliable real-time analytics across pipelines.

Yes, dbt executes data transformations within cross-cloud compatible workflows. It integrates with pipeline orchestration to enforce governance, validate quality, and model data across batch and streaming platforms.

Yes, production-grade data governance and observability support healthcare and finance contexts. It implements robust monitoring, DataOps patterns, and quality controls to satisfy strict enterprise compliance requirements.

You generate an Airflow DAG by defining a YAML configuration and running included Python-based scripts. This scaffolds starter pipelines, creates orchestration logic automatically, and performs initial data validations.

You need streaming architectures instead of batch ETL when workloads demand real-time analytics and immediate data availability. Streaming handles continuous data flows, whereas batch processes discrete scheduled intervals.