Job Description

• Vi is an enterprise AI platform for health enterprises - healthcare, biopharma, and wellness. We deploy agentic AI and predictive models into production environments where the output drives next best actions for patients, care teams, and operations to deliver ROI and improve health outcomes.
• We are looking for a Senior Data Engineer to build and scale the data foundation behind Vi's platform and products. You will own complex data end-to-end - from ingestion and transformation through modeling, quality, observability, and production delivery.
• This is a hands-on senior IC role for a strong builder who can solve difficult data problems independently, set a high technical bar, and collaborate closely with engineering, DS, and product. You will turn large, fragmented datasets into reliable, reusable capabilities that power every Vi product.

Responsibilities

• Build and own scalable data pipelines: Design, implement, and operate robust pipelines for high-volume structured and unstructured data.
• Develop reusable data models: Create normalized, well-documented data layers, shared dimensions, and production-ready datasets for analytics, AI, and predictive modeling use cases.
• Ensure data quality and reliability: Implement automated validation, anomaly and drift detection, lineage, monitoring, alerting, and recovery processes.
• Own production performance: Improve scalability, cost efficiency, security, observability, and operational resilience across the data platform.
• Solve complex problems hands-on: Investigate difficult data inconsistencies, pipeline failures, performance bottlenecks, and architectural challenges directly.
• Partner cross-functionally: Translate product, client, compliance, and business requirements into clear technical designs and dependable production systems.

Requirements

• 5+ years of professional experience in data engineering, including ownership of production data systems.
• Strong Python and SQL skills, with experience writing maintainable, tested production code.
• Deep experience designing and operating ETL/ELT pipelines, data models, and distributed data-processing systems.
• Production experience with Apache Spark or a comparable large-scale processing framework.
• Strong AWS experience, ideally including S3, Glue, EMR, Athena, and related compute and orchestration services.
• Experience with modern data lakehouse or warehouse architectures; experience with Apache Iceberg is a strong advantage.
• Experience with workflow orchestration, CI/CD, Docker, Git, and infrastructure as code such as AWS CDK and CloudFormation.
• Strong understanding of data quality, schema evolution, lineage, observability, privacy, security, and access controls.
• High comfort operating in a fast-moving environment with incomplete requirements, high ownership, and a strong sense of urgency.