VivSoft is hiring a
Data Engineer

Summary

Join our dynamic team as a Data Engineer at VivSoft, where you'll design, implement, and maintain data infrastructure and pipelines. As a key player in building robust data solutions, you'll collaborate with cross-functional teams to deliver high-quality results.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field
• Proven hands-on experience as a Data Engineer, with a strong portfolio of successful data solutions
• Proficiency in Python and other programming languages for data manipulation and analysis
• Strong knowledge and experience with libraries such as Pandas, Numpy, and Scikit Learn
• Solid understanding of databases, including PostGres, SQLServer, MySQL, Oracle, and proficiency in writing efficient SQL queries
• Familiarity with cloud platforms such as AWS, Azure, Google Cloud, and their data services
• Experience working with big data technologies like Spark and Hadoop
• Excellent communication and collaboration skills to work effectively with cross-functional teams
• Secret Clearance is required

Responsibilities

• Design, implement, and maintain data infrastructure and pipelines for efficient data ingestion, profiling, conformance, ETL, analytics, cleansing, classification, visualization, and machine learning
• Collaborate with data scientists, DevSecOps teams, and software engineers to integrate data solutions across environments
• Implement automation for data extraction, transformation, and loading (ETL) processes
• Collect data provenance and integrity requirements from government stakeholders
• Decompose requirements into technical development requirements that can be executed
• Proficiency with Kubernetes or Terraform for infrastructure management and automation
• Work collaboratively with cross-functional teams to understand data requirements and deliver high-quality solutions
• Utilize your problem-solving skills to design scalable and efficient data pipelines
• Stay abreast of industry trends and best practices in data engineering