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# Engineer, Software

- **Company:** Sunrise Systems, Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $185,120.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, ARM Architecture, Cloud Computing, Data Validation, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Github, Apache Hadoop, Apache Hive, Python (Programming Language), NoSQL, Standard Sql, Amazon Simple Notification Service (SNS), SQL Databases, Data Streaming, Talend, Scripting, Freeform SQL, Cloud Platform System, Informatica Powercenter, Snowflake, Apache Spark, Data Lakes, Information Technology, AWS Glue, Functional Programming, Amazon Simple Queue Service (SQS), Software Version Control, Data Pipelines
- **Published:** August 26, 2026
- **Apply:** https://www.dice.com/job-detail/aa91b107-9588-4d98-951b-11c454af3a89

## About the Role

* Bachelor s degree in Computer Science, Engineering, or related field.
 * 8+ years of Data Engineering/ETL experience.
 * Strong SQL, Snowflake, AWS, and data warehouse/data lake experience.
 * Experience with AWS services such as S3, EC2, Lambda, SQS, and SNS.
 * Experience with DBT and cloud ETL tools such as AWS Glue, Informatica, Talend, or Matillion.
 * GitHub/version control experience.
 * Python, Java, or Scala experience is a plus.
 * Knowledge of Spark, Hadoop/Hive, NoSQL, APIs, and streaming is preferred.
 * Strong communication, problem-solving, and teamwork skills.

## Description

Seeking an experienced Senior Data Engineer / AI Engineer to build and optimize ETL/ELT data pipelines using AWS, Snowflake, Snowflake Cortex AI, and DBT., * Design and develop ETL/ELT and event-driven data pipelines.

 * Build data integration solutions using AWS, Snowflake, SQL, and cloud ETL tools.
 * Develop and optimize complex SQL queries and data transformations.
 * Work with Data Analysts, Architects, Data Scientists, and Product Owners.
 * Develop data validation and quality processes.
 * Use GitHub, scripting languages, APIs, and cloud-based data platforms.
 * Troubleshoot and improve data pipeline performance.


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