Principal Data Engineer, Transcarent

• Experienced: 10+ years of experience in data engineering with a strong background in building and scaling data architectures in complex environments. Healthcare experience is a plus
• Technical Expertise: Advanced working knowledge of SQL, relational databases, and big data tools (e.g., Spark, Kafka). Proficient in cloud-based data warehousing (e.g., Snowflake) and cloud services (e.g., AWS). Proficient in understanding of AI / ML workflows, etc.,
• Architectural Visionary: Demonstrated experience in service-oriented and event-based architecture with strong API development skills
• Problem Solver: Ability to manage and optimize processes supporting data transformation, metadata management, and workload management
• Collaborative Leader: Strong communication skills with the ability to present ideas clearly and lead cross-functional teams effectively
• Project Management: Strong project management and organizational skills, capable of leading multiple projects simultaneously
• You must be authorized to work in the United States

Desirable

• Lead the Design and Implementation: Using modern data architecture principles, architect and implement cutting-edge data processing platforms and enterprise-wide data solutions
• Scale Data Platform: Develop a scalable Platform for optimal data extraction, transformation, and loading from various sources, ensuring data integrity and accessibility
• AI / ML platform: Design and build scalable AI and ML platforms to support Transcarent use cases
• Collaborate Across Teams: Partner with Executive, Product, Clinical, Data, and Design teams to meet their data infrastructure needs, supporting them with technical expertise
• Optimize Data Pipelines: Build and optimize complex data pipelines, ensuring high performance, reliability, and scalability
• Innovate and Automate: Create and maintain data tools and pipelines that empower analytics, data science, and other teams to drive innovation and operational excellence
• Mentor and Lead: Provide technical leadership and mentorship to the data engineering team, fostering a culture of continuous learning and improvement