Remote Data Engineer
VivSoft
πRemote - United States
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Job highlights
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
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