PSR Associates, Inc.
is an IT consulting firm specializing in Staffing and Recruiting Services. People. Solutions. Results. Founded in 2003, PSR Associates, Inc. is headquartered in Atlanta, GA, with additional offices in Tampa, FL; Washington, D.C.; Charlotte, NC; Austin, TX; and Irvine, CA.
Data Engineer - Data Lab
The Data Lab focuses on implementing solutions that impact efficiency and effectiveness of our clients' functions as they relate to storage, cataloging, and analytics. Process improvement, transformation, effective use of technology and data & analytics, and leveraging alternative delivery are key areas to drive value and continue to be recognized as the leading professional services firm. Data Labs is focused on identifying and prioritizing emerging technologies and preparing clients to get the most out of their emerging technology investments.
Global Competency Network:
Data and Analytics Technologies
Job Requirements and Preferences:
Minimum Degree Required:
Additional Educational Requirements:
In lieu of a Bachelor Degree, 12 years of professional experience involving technology-focused process improvements, transformations, and/or system implementations
Minimum Years of Experience:
Preferred Fields of Study:
Business Analytics, Computer and Information Science, Mathematics
Demonstrates thorough abilities and/or a proven record of success as a team leader including the following areas:
- Demonstrates thorough knowledge and/or a proven record of success in the following areas:
- Azure Cloud computing platforms.
- Relational databases and writing SQL queries.
- Big data machine learning toolkits such as SparkML, messaging systems (Kafka) and NoSQL databases (Cosmos DB, Cassandra, HBase, MongoDB); and,
- Building data lakes.
- Data transfer systems such as Azure Data Factory, SnapLogic and Apache NiFi
- Having a background in computer science and comfortable in programming in a variety of languages, including Java, Python, Scala and/or C#/.NET.
- Determining the appropriate software packages or modules to run, and how easily they can be modified.
- Handling large scale structured and unstructured data from internal and third-party sources.
- Architecting highly scalable distributed data pipelines using open source tools and big data technologies such as Hadoop, Pig, Hive, Presto, Spark, Drill, Sqoop and ETL frameworks.
- Utilizing Linux shell scripting and containerization technologies (Docker, Kubernetes); and,
- Leading teams in a dynamic work environment while managing stakeholder expectations and scope.