• Data engineer with a solid technical
  background and banking experience in data-intensive systems (>7 years)
  
• Strong experience in Big Data, Hadoop
  Ecosystem, Spark Streaming, Kafka, Python, SQL, Hive, NIFI, Airflow
  
• Proficient with Azure Cloud services
  such as Azure Data Factory (ADF), Databricks, ADLS, Azure Synapse, Logic Apps,
  Azure Functions. Or similar data stack knowledge within Google/AWS cloud
  services
  
• Proficiency in relational SQL, Graph
  and NoSQL databases.
  
• Proficiency in Elastic Search and
  Couchbase databases
  
• In-depth skills in developing and
  maintaining ETL/ELT data pipelines.
  
• Experience in data modelling
  techniques such as Kimball star schema, 3NF, vault modelling etc.
  
• Experience in workflow management
  tools such as Airflow, Oozie and CI/CD tools
  
• Data streaming solution in Kafka or
  Confluent Kafka
  
• Hands on experience in Google Big
  Query, Google Analytics & Clickstream Data Model
  
• Reporting knowledge in Power Bl,
  Tableau, Qlik etc.
  
• Sound in Data Management Fundamentals
  and Data Architect, Modelling, Governance
  
• Strong Domain Knowledge in
  Banking/Finance area like understanding of the various processes, products, and
  services within the banking industry, core functions, regulations, and
  operational aspects of banking institutions.
  
• Hands on knowledge in Hadoop and
  Azure/AWS cloud ecosystem and ETL jobs migration
  
• Knowledge of Advance Analytics and Al
  tools
  

Requirements

Benefits