Basic

• Work experience with ETL, Data Modeling, and Data Architecture.
  Experience with Big Data technologies such as Hadoop/Hive/Spark.
  Skilled in writing and optimizing SQL.
  Experience operating very large data warehouses or data lakes.

Preferred

• Experience in designing and implementing highly performant data ingestion pipelines from multiple sources using Apache Spark and/or Azure Databricks
• Show efficiency in handling data - tracking data lineage, ensuring data quality, and improving discoverability of data.
  Integrating end-to-end data pipeline to take data from source systems to target data repositories ensures the quality and consistency of data is always maintained.
  Knowledge of Engineering and Operational Excellence using standard methodologies.
• Comfortable using PySpark APIs to perform advanced data transformations.
• Familiarity with implementing classes with Python.

Summary

Design, build and operationalize large scale enterprise data solutions and applications using one or more of AWS data and analytics services in combination with 3rd parties - Spark, EMR, RedShift, Lambda, Glue
Design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, and Python.
Design and implement data engineering, ingestion, and curation functions on AWS cloud using AWS native or custom programming.