Senior Software Engineer (Data Solutions) REMOTE

Senior Software Engineer (Data Solutions) REMOTE

We have an exciting opportunity for a Senior Software Engineer (Data Solutions) REMOTE to help Myriad Genetics, Inc. deliver innovative products that allow patients to make life-changing decisions. Our genetic screening and testing help to provide actionable insights to empower people to make critical and timely healthcare decisions by using molecular diagnostic tests for hereditary cancer, urological cancer, depression, and other diseases. We are excited about the future and remain committed to advancing the science of personalized medicine as we develop more products to address unmet medical needs. 

This role on the Myriad Data Services Team demands a deep understanding of software development, data engineering principles, and architectural design. They are responsible for architecting and developing data solutions and data engineering pipelines. They work cross-functionally with R&D, technology, and business operations teams to implement data-driven solutions that enhance patient care, drive business insights, and improve operational efficiencies. This role requires a strategic and innovative leader capable of driving impactful solutions across both scientific and business functions.

Responsibilities

• Design, develop, and deploy high-quality software solutions that meet business requirements.
• Build and maintain robust data pipelines and infrastructure to support data-driven decision making by leveraging AWS technologies.
• Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.
• Collaborate with engineers to help adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentation
• Design and develop data products for reusability and lead data mart standardization
• Enforce industry best practices and discipline to Implement Data-as-a-Service/Product architecture enabling advanced analytics (AI/ML) for business analysis and innovation.
• Implement appropriate data architecture and data modeling to turn data into a strategic asset and build advanced data solutions and capabilities to foster data-driven decision-making.
• Collaborate with cross-functional architects as a true business and strategic partner and enabler of the organization.
• Define access control policies, encryption policies, and archival policies as per industry standards.

Responsibilities:

• Designing Data Architecture:
  • Create Data Models: Develop conceptual, logical, and physical models defining how data will be stored, processed, and accessed.
  • Design Data Storage Solutions: Ensure that the data is stored in a way that is both efficient and scalable, using technologies like relational databases, NoSQL databases, data lakes, or cloud storage solutions.
  • Establish Data Integration Frameworks: Plan how data will flow between systems, ensuring that the architecture supports smooth data integration, transformation, and movement.
• Software Development:
  • Plan, design, analyze, build, maintain, test and enhance 
    • software systems of both new and existing products/modules.
    • infrastructure, architecture, and/or technical strategy across multiple products and teams.
• Data Engineering:
  • Collaborate in technical vision and development of a foundation, framework or capability of the team.
  • Own technical vision and development of a foundation, framework or capability of the team.
  • Independently, build architecture for simple to moderate systems. Understand architecture for moderate to complex systems and build in collaboration with other architects.
  • Understand end-to-end architecture for complex systems. Guides data architecture to teams across the organization.
  • Expert in Cloud technologies – Example: AWS
  • Experience with the tech stack – Python & Snowflake is must. SQL and dbt is a plus.
• Data Governance and Security:
  • Ensure Data Quality by implementing strategies to ensure the data remains accurate, consistent, and reliable.
  • Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA), security protocols and implement strong data governance practices to ensure data security
  • Implement robust data governance practices and ensure compliance with industry regulations (e.g., HIPAA for healthcare data).
  • Define and promote best practices. 
• Collaboration with Stakeholders:
  • Collaborate with business analysts, data engineers, and other stakeholders