VP Engineering (Data Strategy & Product Management)

Overview

We are seeking an experienced and visionary vice president of data ingestion engineering to lead our engineering team and be responsible for one of the most critical products in our portfolio. Our data ingestion is a sophisticated data ingestion solution within the Cloudera ecosystem, responsible for processing 50% of the medical claims in the country. This role demands a leader with deep technical expertise in big data, Cloudera, Master Data Management, and modern data architecture approaches such as data fabric and data mesh. Additionally, experience in healthcare data management, particularly with FIHR and HL7 standards, is crucial. The VP will also oversee the integration of advanced reporting and analytics using the latest AI/ML tools from downstream teams. 

Responsibilities

• Leadership & Management: 

• Lead and manage a team of full-stack developers and Master Data Management (MDM) developers, ensuring the successful ingestion, normalization, and processing of vast amounts of healthcare data, particularly medical claims. 

•  Oversee the entire Cloudera stack, ensuring that data ingestion operates efficiently and effectively, meeting all performance, scalability, and security requirements specific to healthcare data. 

•  Drive the vision and strategic direction of the proprietary product, aligning with broader company goals and the evolving needs of the healthcare data landscape, incorporating modern data architecture principles such as data fabric and data mesh. 

•  Collaborate closely with AI/ML teams downstream to ensure that data is structured and available for advanced analytics and reporting, leveraging the most modern BI, AI, ML tools and technologies. 

 

• Technical Oversight: 

•  Ensure seamless ingestion and conversion of diverse healthcare data formats and structures from multiple sources into the Global Data Format (GDF), adhering to standards such as FIHR and HL7. 

•  Lead the architecture and development efforts for data ingestion, focusing on scalability, reliability, and innovation within the Cloudera ecosystem, with an emphasis on healthcare data requirements and modern data architectures like data fabric and data mesh. 

•  Drive initiatives to enhance data storage, retrieval, and analytics capabilities, leveraging the full power of the Cloudera stack, particularly for healthcare data analytics, and ensuring seamless integration with AI/ML models and tools. 

•  Oversee Master Data Management processes to ensure healthcare data is normalized and ready for use by downstream developers and analysts, ensuring compliance with FIHR and HL7 standards. 

•  Collaborate with AI/ML teams to implement advanced reporting and data analytics, ensuring data infrastructure supports cutting-edge innovations in healthcare data analysis. 

 

• Strategic Vision: 

• Develop and execute strategic initiatives that advance data ingestion capabilities, including exploring new technologies and methodologies in big data, cloud computing, data fabric, data mesh, and AI/ML, with a specific focus on healthcare data. 

• Act as a thought leader within the company, providing insights and guidance on data-related strategies, similar to the role of a Enterprise Data strategist, with a particular focus on healthcare. 

• Ensure the data engineer product aligns with the latest industry standards, regulatory requirements, and technological advancements in healthcare, including FIHR, HL7, data fabric, data mesh, and AI/ML integration. 
• Strategy for Prepay vs. Post-Pay Data Feeds: Develop and execute a strategy for use of prepay versus Post-Pay data feeds based on client needs and product requirements.

 

• Stakeholder Engagement: 

• Work closely with healthcare clients, partners, and internal teams to understand data requirements, ensuring NILE meets and exceeds expectations in processing and managing healthcare data. 

• Represent data ingestion in discussions with senior leadership, providing updates on product performance, challenges, and opportunities, particularly in relation to healthcare data management, advanced analytics, and AI/ML integration. 

 

• Develop Criteria & Assess Quality: Establish and implement criteria to assess the quality of client data feeds, ensuring they support product requirements and deliver on the client value proposition
• Change Control Process: Establish an effective change control process for the Global Data Format to manage version control and ensure optimal cadence for upgrades, ensuring seamless transitions and minimal disruption.
• Architecture Roadmap: Create and maintain