Senior DataOps Engineer

A Specialty Path to Good Health

Upperline Health is the nation’s largest provider dedicated to lower extremity, wound and vascular care. Founded in 2017 with the ambitious goal of changing specialty care,Upperline Health delivers a more efficient path for patients to receive consistent and effective treatment for chronic illnesses.

Upperline Health providers coordinate patients’ care among a team of specialists – physicians, advanced practice providers, care navigators, pharmacists, dieticians, and social workers for integrated treatment that addresses patients’ immediate and long-term health needs.

Working within enterprise architecture and security standards and alongside modern full-stack engineers, including React/Type Script developers, this person translates product requirements into scalable, secure, well-instrumented data services. The ideal candidate combines hands-on database and platform depth with architectural judgment, operational ownership, and a bias toward automation and incremental delivery.

• Own the architecture and evolution of the application data layer, including logical and physical models, schemas, storage strategies, access patterns, service boundaries, and technology selection.
• Design transactional and operational models that preserve integrity, support concurrency and workflow state changes, and appropriately balance normalization, auditability, and performance.
• Model identity, authentication, and authorization data with security and application teams, including users, roles and permissions, sessions or token metadata, login events, account status, and audit history.
• Design application-utilization and product-telemetry models for events, feature usage, workflow progression, adoption, errors, and performance, with appropriate privacy and retention controls.
• Design and implement, or guide implementation of, application-facing APIs and data services with clear contracts for validation, pagination, filtering, versioning, idempotency, errors, and backward compatibility.
• Design caching and shared-state strategies using Redis or comparable technologies, including keys, time-to-live policies, invalidation, consistency, graceful degradation, and observability.
• Own database and query performance through schema design, indexing, execution-plan analysis, partitioning, connection pooling, ORM and query-pattern review, N+1 prevention, and capacity planning.
• Establish standards for schema evolution, migrations, seed and reference data, rollback, compatibility, and zero- or low-downtime deployment.
• Define safe synchronization between operational application stores and analytical platforms through change data capture, events, replication, and backfill patterns that protect application performance.
• Evaluate data stores, API infrastructure, and supporting services for scalability, availability, recoverability, security, maintainability, performance, and cost.

• Implement and continuously improve data connectors, ingestion jobs, and orchestration workflows according to established enterprise patterns.
• Build and maintain CI/CD for data pipelines, database migrations, APIs and data services, and environment configuration across development, staging, and production.
• Own the production lifecycle of application data services and ingestion systems, including on-call participation, alert tuning, incident response, root-cause analysis, and corrective action.
• Implement automated data-quality, schema, and contract checks at ingestion and application-service boundaries.
• Monitor and…