Lead Data Engineer Predictive AI

** REMOTE ROLE for Contractor anywhere in N. America **

** 12 month contract and possible extension **

** Rate = $80 - 90/hr USD Based On Experience 

Description:

Data Engineers at TA Digital work closely with Subject Matter Experts (SMEs) to design the ontology (data model), develop data pipelines, and integrate Foundry with external systems containing the data. Data engineers also need to provide guidance and support on how to access and leverage the data foundation to create new workflows or analyze data.

Responsibilities Include:

• Hands-on project experience with Palantir Foundry, including data integration, data modeling, and application development
• Strong Python experience with expertise in using Pandas, NumPy, and PySpark
• Expertise in machine learning and predictive analytics, with hands-on experience in developing and deploying ML models
• Confident to review/approve pull requests and provide guidance for developers on the Predictive AI team
• Background in data science preferred
• Helps identify opportunities within their platform (Palantir Foundry) and work with Product and Digital Technology leadership to address.
• Strong communicator and collaborator able to educate Product and other stakeholders on predictive AI capabilities in general and Palantir Foundry in specific, and how these capabilities are integrated with AARP's digital platforms
• Works with requirements from Product and assist developers with guidance.
• Challenge and collaborate with Product on requirement modifications where appropriate

Requirements:

• Palantir Foundry, including data integration, data modeling, and application development
• Generative AI on AWS such as Amazon Bedrock, Amazon SageMaker, Amazon EC2, Amazon EC2 UltraClusters, AWS Trainium or AWS Inferentia
• Python – complete language proficiency 
• SQL – proficiency in querying language (join types, filtering, aggregation) and data modeling (relationship types, constraints)
• PySpark – basic familiarity (DataFrame operations, PySpark SQL functions) and differences with other DataFrame implementations (Pandas)
• Distributed compute – conceptual knowledge of Hadoop and Spark (driver, executors, partitions)
• Databases – general familiarity with common relational database models and proprietary instantiations, such as SAP, Salesforce etc.
• Git – knowledge of version control / collaboration workflows and best practices
• Iterative working – familiarity with agile and iterative working methodology and rapid user feedback gathering concepts
• Data quality – best practices