Senior Data Platform Engineer

Qventus•San Francisco, CA

About The Position

On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient care. Our innovative solutions harness the power of machine learning, generative AI, and behavioral science to deliver exceptional outcomes and empower care teams to anticipate and resolve issues before they arise. Our success in rapid scale across the globe is backed by some of the world's leading investors. At Qventus, you will have the opportunity to work with an exceptional, mission-driven team across the globe, and the ability to directly impact the lives of patients. We’re inspired to work with healthcare leaders on our founding vision and unlock world-class medicine through world-class operations. #LI-JB1 As a Senior Data Platform Engineer, you will be instrumental in driving the strategic evolution and design of our data platform investments and data pipelines, working in close partnership with Architects. You will provide technical leadership in identifying, monitoring, and driving initiatives that ensure our data platform (including core pipeline frameworks, warehousing, and ML Ops platform) remains scalable, reliable, and efficient amidst evolving product demands You will work closely with solution experts to identify missing platform functionality and areas for organizational optimization and lead new build or optimization initiatives. You will be adept in partnering with cross-functional partners and data users to translate needs into technical solutions and lead the technical scoping, implementation, and general execution of improvements to our solutions and platform. You will be data curious and excited to impact the team and the company and improve the quality of healthcare operations.

Requirements

Strong cross-functional communication - ability to break down complex technical components for technical and non-technical partners alike
4+ years of hands-on experience designing, building, and operating cloud-based, highly available, observable, and scalable data platforms utilizing large, diverse data sets in production to meet ambiguous business needs
Excellence in quality data pipeline design, development, and optimization to create reliable, modular, secure data foundations for the organization's data delivery system from applications to analytics & ML
Experience building, designing, and/or developing on a diverse set of modern data architecture designs and their relative capabilities and use cases (ex. Data Lake, Lakehouse)
Experience working with Databricks and deployed production grade pipelines
Python and DBT
SQL

Nice To Haves

Proficiency in interpreting complex datasets, including the ability to discern underlying patterns, identify anomalies, and extract meaningful insights, demonstrating advanced data intuition and analytical skills with the ability to translate these insights into recommendations for platform improvements that align with the overall architecture
Relevant industry certifications in various Data Architecture services (SnowPro Advanced Architect, Azure Solutions Architect Expert, AWS Solutions Architect / Database, Databricks Data Engineer / Spark / Platform etc.)
Experience designing and supporting multi-cloud architectures (particularly for ML / AI systems)
Experience with data visualization tools and analytics technologies (Sigma, Looker, PowerBI, etc.)
Degree in Computer Science, Engineering, or related field
Experience working with healthcare data and HIPAA data protection

Responsibilities

Lead scoping and execution of critical improvements to our data platform to maintain overall system health and improve data observability in lieu of changing product needs, and to optimize innovation velocity
Support production ML Ops functionality and advance the quality of our core ML & LLM platform capabilities
Partner strategically with data science, analytics, and data engineering leads and Architects to gather feedback and drive the development of scalable platform solutions that unlock new features within the defined architectural framework.
Provide expertise on the overall data engineering best practices, standards, architectural approaches and complex technical resolutions
Support solution development; translate product / analytical vision into highly functional data pipelines supporting high quality & highly trusted data products (incl. designing data structures, building and scheduling data transformation pipelines, improving transparency etc.).