Data Engineer with Security Clearance
3 weeks ago
Active TS (SCI Eligible) Clearance Required Data Science professional services practice focused on strategy, technology, and solving business challenges with Artificial Intelligence (AI)
The team has a proven track record serving large, market-leading organizations in the private and public sectors, successfully delivering high-quality, novel and complex projects, and offering deep domain and scientific capabilities
Made up of experienced AI strategists, data scientists, and AI engineers, they serve as trusted advisors to executives, helping them understand and evaluate new and essential areas for AI investment and identify unique opportunities to transform their businesses
Job Duties: • Work with clients to design, develop, and deploy new architectures for machine learning & automation applications such as ELT functions, HPC/compute infrastructure, AWS/Cloud solutions, database solutions, and optimization of DevOps procedures • Leverage skills in modern data architecture, cloud engineering, data transformation, and management of structured and unstructured data sources • Support and enhance data architecture, and data pipelines, and define database schemas (Graph DB, SQL, NoSQL) to support algorithm scalability and deployment based on agile business priorities and technology initiatives • Participate in architectural discussions to ensure solutions are designed for successful deployment, security, and high availability in the cloud or on-prem • Adopt and maintain best engineering practices in automation, HPC, CI/CD, and AIOps Required: • Bachelor's degree in a STEM field or equivalent experience (Computer Science, Engineering, Physics etc.); Master's degree preferred • 4+ years' experience in data engineering, cloud engineering, MLOps, while building highly scalable and secure solutions • Proficient in Python, SQL, Shell scripting • Experience with distributed computing frameworks (e.g., Spark, Dask), cloud platforms (e.g
AWS, Azure), containerization, and supporting analytics libraries • Expertise with code management and DevOps tools (e.g., Docker, Kubernetes, Jenkins, etc.) • Experience with workflow and data management solutions such as Airflow, Kafka, Glue, etc
• Experience designing data architectures and understanding of different types of databases or platforms (relational, NoSQL, graph, etc.) • Strong analytical and problem-solving skills with the ability to develop novel and efficient solutions
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