Sr Datastage & Netezza Developer & Engineer,- Remote

Simple Solutions; Jacksonville, Northern (FL, KY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Simple Solutions in Jacksonville, FL is seeking an experienced Data Engineer focused on ETL development with DataStage and Netezza. You will design, develop, and optimize complex DataStage jobs to load data from diverse sources into a Netezza data warehouse, applying ELT techniques and SQL tuning.

You will also implement validation, scripting, and automation for reliable data pipelines, while collaborating with cross-functional teams on performance improvements and production support.

Qualifications

• Bachelor’s degree in CS/IT or related field.
• 5-8 years data engineer experience in ETL development, with 2+ years on DataStage-Netezza integration.
• Deep understanding of Netezza MPP architecture, S-Blades, and FPGA processing.
• Proven ability to debug complex data issues and optimize long-running ETL workflows.
• Knowledge of star/snowflake schemas, Dim & Fact tables, and SCD Type 1/2.
• Experience with scheduling, version control, and Netezza tools.
• Nice to have Snowflake or Databricks experience.

Responsibilities

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

Skills

ETL development

DataStage

Netezza

SQL tuning

Unix/Linux shell scripting

Data warehousing concepts

SCD 1/2

Git/SVN

Control-M/AutoSys

Netezza Workbench/Aginity

Snowflake

Databricks

Education

Bachelor’s degree in CS/IT or related field

Tools

Control-M

AutoSys

Git

SVN

Aginity

Netezza Workbench

Job description

Jacksonville, United States | Posted on 02/20/2026

some experience with Snowflake or Databricks will be preferred.

• 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
  
• 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