Data Science, Advisor

Peraton•,

1d•$135,000 - $216,000•Hybrid

About The Position

Peraton is looking to hire a Data Science Engineer in the Washington DC Metro area. This role will be a remote position. At times, the role will also require travel to the Quantico client site when necessary. The Data Science Engineer designs, builds, and scales an organization's foundational data infrastructure. Beyond simply constructing data pipelines, this role involves supporting technical activities across the contract, driving continuous improvement and innovation into program operations, and advising on technology alternatives related to processing, data storage, data access, application development, and enterprise architecture decisions. The successful candidate will establish coding best practices and align complex data architectures with overarching business goals, utilize APIs to push and pull data from various data systems and platforms, and perform general data manipulation skills such as reading, processing, cleaning, transforming, merging, and reformatting data. Additionally, the role involves building and optimizing ETL/ELT pipelines using glue and python, packaging, curating, and versioning datasets for fine-tuning and model training jobs on Bedrock and Sagemaker, and enforcing CUI handling requirements, automating PII detection, and maintaining audit trails, e.g. Amazon Macie.

Requirements

8 years experience with a BS/BA, 6 years with a MS/MA, 3 years with a PhD or 12 years of experience in lieu of a degree
Active Secret clearance
Demonstrates strong expertise in AWS architecture, security in the SDLC, and communication of technical risk and architecture decisions to executive audiences.
Experience designing and building Data Lakes, Data Warehouses, and scalable data platforms in the cloud
Experience with programming skills like Java, SQL, Scala, Python, R, defining schema and software engineering best practices including secure, testable, and maintainable code and database (eg SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis)
Demonstrate a 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, ex. find answers to common programming challenges on Google etc.
Ability to work effectively in teams, in both a lead and support role as needed
Must be local to the Washington DC Metro area

Nice To Haves

Experience designing and building Data Lakes, Data Warehouses, and scalable data platforms in the cloud
Excellent listening, interpersonal, communication and problem solving skills.

Responsibilities

Designs, builds, and scales an organization's foundational data infrastructure.
Supports technical activities across the contract, and drives continuous improvement and innovation into program operations.
Advises on technology alternatives related to processing, data storage, data access, application development, and enterprise architecture decisions as needed.
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.
General data manipulation skills: read in data, process and clean it, transform and recode it, merge different data sets together, reformat data between wide and long, etc.
Build and optimize ETL/ELT pipelines using glue and python to move and transform data across fabric.
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.