Data Science Advisor

Responsibilities Design, build, and scale an organization’s foundational data infrastructure. Design and build Data Lakes, Data Warehouses, and scalable data platforms in the cloud. Support technical activities across the contract. Drive continuous improvement and innovation into program operations. Advise on technology alternatives related to processing, data storage, data access, application development, and enterprise architecture decisions. Establish coding best practices and align complex data architectures with overarching business goals. Use APIs to push and pull data from various data systems and platforms. Build and optimize ETL/ELT pipelines using Glue and Python. Package, curate, and version datasets for LM fine‑tuning and model training jobs on Bedrock and SageMaker. Enforce CUI handling requirements, automate PII detection, and maintain audit trails (e.g., Amazon Macie). Requirements 8 years of experience with a BS/BA, 6 years with a MS/MA, or 3 years with a PhD, or 12 years of experience in lieu of a degree. Active Secret clearance. Strong expertise in AWS architecture and security in the SDLC. Ability to communicate technical risk and architecture decisions to executive audiences. Experience designing and building Data Lakes, Data Warehouses, and scalable data platforms in the cloud. Programming skills in Java, SQL, Scala, Python, and R. Defining schema and software engineering best practices including secure, testable, and maintainable code and database design (e.g., SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis). Deep understanding of data engineering principles and techniques. Ability to work effectively in teams, in both a lead and support role. Ability to learn new techniques and troubleshoot code without support. Must be local to the Washington DC Metro area. #J-18808-Ljbffr Jobtailor