Sr Datastage & Netezza Developer & Engineer,- Remote

 Posted 4 months ago
     
5-10 years experience
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AI Summary

The role involves designing and developing complex DataStage jobs for data extraction and loading into Netezza, alongside optimizing these jobs and performing specific Netezza SQL tuning. Responsibilities also include developing automation scripts and providing production support for troubleshooting and root cause analysis.

POSITION SUMMARY


Sr Datastage & Netezza Developer & Engineer,
some experience with Snowflake or Databricks will be preferred. 


RESPONSIBILITIES

Qualifications

  • Bachelor’s degree in Computer Science, Information Technology, or a related field
  • 5-8 years of data engineer experience in ETL Development, with at least 2 years specifically focused on the DataStage-Netezza integration
  • Deep understanding of Netezza’s MPP architecture, S-Blades, and FPGA processing
  • Proven ability to debug complex data issues and optimize long-running ETL workflows
  • Advanced SQL (Netezza-specific SQL), Unix/Linux Shell Scripting
  • Knowledge of Star/Snowflake schemas, Dim & Fact tables, and SCD (Type 1/2)
  • Control-M or AutoSys (Scheduling), Git/SVN (Version Control), Aginity (Netezza Workbench)
  • Nice to have - some experience with Snowflake or Databricks will be preferred. 


Responsibilities

  • Design and develop complex DataStage jobs (Parallel and Sequence) to extract data from various sources (Flat Files, DB2, SQL Server, APIs) and load them into Netezza
  • Optimize DataStage jobs using partitioning, sorting, and buffer tuning
  • Perform SQL tuning specifically for Netezza (e.g., managing Distribution Keys, Clustered Base Tables, and minimizing data skew)
  • Utilize the Netezza Connector and Netezza Performance Server features within DataStage to implement high-speed data loading (nzload) and external table frameworks
  • Write advanced Netezza SQL, including stored procedures, complex joins, and analytical functions to handle data transformation directly within the warehouse (ELT approach)
  • Develop Unix/Linux shell scripts to automate job scheduling, file management, and error handling
  • Implement data validation and cleansing rules to ensure the integrity and accuracy of the data warehouse
  • Provide production support, troubleshoot job failures, and perform root cause analysis for performance bottlenecks


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