ETL with Python, Spark & Hadoop

Responsibilities.
Organize business needs into ETL/ELT logical models and ensure data structures are designed for flexibility to support scalability of business solutions
Craft and implement data pipelines utilizing Spark and Python
Define and deliver. Reusable components for ETL/ELT framework.
Define optimal data flow for system integration and data migration
Integrate new data management technologies and software engineering tools into existing structures
Design, build and maintain CI/CD pipelines in multiple integration and test environments
Install, configure and manage automated testing tools in the environment
Qualifications
Experienced in Design, Development, and Implementation of large-scale projects in financial industries using Data Warehousing ETL tools (Spark)
Experience in creating ETL transformations and jobs using PySpark and automating workflows using Orchestration tools like Airflow, Control-M
Strong knowledge and experience of SQL, Python and Spark
Experience with Big Data/distributed frameworks such as Spark, Kubernetes, Hadoop, and Hive
Ability to design ETL/ELT solutions based on user reporting and archival requirements
Strong sense of customer service to consistently and effectively address client needs
Self-motivated; comfortable working independently under general direction
Hands-on experience in building and managing CI/CD pipelines
Basic knowledge of Azure Cloud Components