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 Sage Maker.
• Enforce CUI handling requirements, automate PII detection, and maintain audit trails (e.g., Amazon Macie).

• 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, No
  
  SQL, 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.