Demonstrated experience using big data processing technologies such as Spark, PySpark, and Python.
Demonstrated experience with data mapping, extraction, transformation, and loading.
Demonstrated experience building analytic reports in tools such as CloudWatch and Kibana.
Demonstrated experience processing and converting operating system and data logs into reports and metrics dashboards.
Demonstrated experience with regular expressions (RegEx).
Demonstrated experience with SQL, MySQL, and PostgreSQL.
Demonstrated experience processing XML and JSON data files.
Demonstrated experience with integrated development environments and data modeling through notebooks and Visual Studio.
Demonstrated experience ETL-ing data from disparate structured and unstructured formats into enriched, query-friendly structured data in indexed files.
Demonstrated experience performing extensive data review and data quality analysis.
Demonstrated experience developing ETL design documentation, including source and target mapping and data dictionary information.
Demonstrated experience interfacing with customers and integration partners to gain and clarify detailed objectives.
Demonstrated experience supporting Agile development by contributing to tasking definition, scope, and review.
Responsibilities
Support the customer with requirements for new system capabilities, the extension and maintenance of bulk data pipelines, and enhancements for multiple applications in the customer's cloud computing environment.
Desired Qualifications
Demonstrated experience deploying capabilities on the Databricks unified analytics platform.
Demonstrated experience with Elastic MapReduce to run big data workloads.
Demonstrated experience joining multiple complex data sets using Spark.
Demonstrated experience tuning Spark streaming and batch jobs for cluster utilization and speed.
Demonstrated experience with Python data analysis libraries such as Pandas.
Demonstrated experience using cloud services such as Lambda, SNS/SQS, or EC2.
Demonstrated experience with DevOps tools including CloudWatch, Lambda, SQS, DynamoDB, and RDS.
Demonstrated experience working with Elastic, Elasticsearch, Logstash, or Kibana (ELK stack).
🔒 Clearance & Citizenship Requirements
This position requires U.S. Citizenship and an active TS/SCI security clearance with Full Scope Polygraph. Candidates who do not currently hold these credentials will not be considered.
Description:
Support Customer with the requirements for new system capabilities, the extension and maintenance of bulk data pipeline and enhancements for multiple applications in the customers' cloud computing environment.
Mandatory Skills:
1. Demonstrated experience utilizing big data processing tech such as Spark, Pyspark, and Python.
2. Demonstrated experience data mapping, extraction, transformation and loading.
3. Demonstrated experience building analytic reports in tools such as CloudWatch and Kibana.
4. Demonstrated experience processing and converting of OS and Data Logs into reports and metrics Dashboards.
5. Demonstrated experience with Regular Expressions (RegEx).
6. Demonstrated experience with SQL, MySQL, PostgresSQL.
7. Demonstrated experience with Data file type processing XML, JSON.
8. Demonstrated experience with IDEs and Data Modeling through Notebooks and Visual Studio.
9. Demonstrated experience ETL’ing data from disparate structured & unstructured data formats into enriched, query-friendly structured data in indexed files.
10. Demonstrated experience performing extensive data review and data quality analysis.
11. Demonstrated experience developing ETL design documentation including source and target mapping and data dictionary information.
12. Demonstrated experience interfacing with customers and integration partners for gaining and clarifying detailed objectives.
13. Demonstrated experience supporting Agile development by contributing to tasking definition, scope and review.
Optional Skills:
1. Demonstrated experience deploying capabilities on the Databricks unified analytics platform.
2. Demonstrated experience with Elastic Map Reduce to run big data workloads.
3. Demonstrated experience in joining multiple complex data sets using Spark.
4. Demonstrated experience in tuning Spark streaming and batch jobs for cluster utilization and speed.
5. Demonstrated experience in deploying complex, notebook-based pipelines.
6. Demonstrated experience in Python data analysis libraries such as Pandas.
7. Demonstrated experience utilizing Cloud services such as Lambda, SNS/SQS, or EC2.
8. Demonstrated experience with DevOps tools to include Cloudwatch, Lambda, SQS, Dynamo and RDS.
9. Demonstrated experience working with Elastic, ElasticSearch/Logstash/Kibana (ELK stack).