Experience should be between 7-10 yrs. At least 4 years relevant experience in data engineering is mandatory
Ability to Create and maintain optimal ETL/ELT data pipelines using IBM datastage , AWS Glue/ AZure ADF/GCP Dataflow/Hadoop
At least experience with one cloud/Hadoop ecosystem is mandatory apart from IBM datastage
Good Experience in data warehousing using AWS Redshift or Azure Synapse/SQLDW or GCP Bigquery or Hadoop Data Warehousing
Very Strong SQL knowledge and experience working with relational SQL and NoSQL databases.
Very strong knowledge in implementing data transformations using Stored procedures
Good Experience in IBM datastage apart from Cloud data warehousing is a strong plus
Good Experience in migrating legacy mainframe data to modern data warehousing is a strong plus
A successful history of manipulating, processing and extracting value from large disconnected datasets.
Ability to understand requirements and create required data Models and data Mapping documents, tables and other sql objects needed.
Ability to Write unit/integration tests, contributes to engineering wiki, and documents work.
Performs data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
Ability to Implement processes and systems to monitor data quality, ensuring production data is always accurate.
Collaborates with analytics and business teams to improve data models that feed business intelligence tools
Strong analytic skills related to working with structured/unstructured datasets.
Knowledge on Visualization/Reporting tools like Power BI/AWS Quicksight /GCP Datastudio is an added advantage
knowledge on real time stream processing using Spark/Kafka is an added advantage
B.E / B. Tech / MCA / Masters in computer science or any other equivalent experience is preferred.
Keyskills: Computer science Data analysis Datastage Data quality Stored procedures data mapping Business intelligence Analytics Reporting tools SQL