Note: The job is a remote job and is open to candidates in USA. Upperline Health is a healthcare provider focused on lower extremity, wound, and vascular care through integrated, value-based treatment. The Senior DataOps Engineer will architect, build, operate, and optimize application data layers, databases, APIs, ingestion systems, and data services while ensuring reliability, security, observability, and scalable collaboration across engineering and product teams.
Responsibilities
- 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 alert on pipeline health, freshness, database availability and performance, query and API latency, error rates, connection pools, cache health, replication lag, and application data quality
- Define and test backup, restore, disaster-recovery, retention, load-testing, and capacity-planning procedures aligned with service objectives
- Maintain runbooks, architecture diagrams, data dictionaries, data contracts, migration procedures, and infrastructure-as-code automation that reduce drift and manual intervention
- Partner with Product and Application Engineering to translate workflow and user-experience requirements into durable data models, APIs, and operational data services
- Work effectively with React/TypeScript and other full-stack engineers by understanding client data consumption, state-management needs, API behavior, and frontend performance implications well enough to design practical interfaces
- Partner with Data Engineering on backfills, schema evolution, change data capture, operational-to-analytical movement, downstream quality assertions, and safe use of application data for reporting
- Partner with Security and Platform Engineering on identity-provider integrations, OAuth 2.0 and OpenID Connect patterns, secrets, encryption, network controls, least-privilege access, and environment configuration
- Lead architecture, data-model, API-contract, and code reviews; communicate technical decisions, tradeoffs, risks, incidents, and migration plans clearly across teams
- Use Git with pull requests, protected branches, required reviews, automated checks, and traceable release practices
- Establish automated tests for database migrations, data contracts, APIs, integrations, performance, and data quality based on component risk
- Build small, modular, backward-compatible components that favor fast feedback and safe deployment over monolithic frameworks
- Apply secure-by-design, observability, service-level, and DataOps practices that improve cycle time, reliability, performance, and operational clarity
Skills
- • Strong experience architecting, building, and operating production application data layers, databases, data services, and ingestion systems
- • Advanced SQL and relational database experience, with strong logical and physical modeling skills for transactional and operational workloads
- • Hands-on experience with transactions, consistency, concurrency, indexing, query plans, partitioning, connection management, and production query optimization
- • Experience designing and implementing application-facing APIs or data-access layers, including RESTful APIs and/or GraphQL, contracts, versioning, validation, and error semantics
- • Experience designing and operating Redis or comparable caching, including invalidation, time-to-live policies, consistency, and failure handling
- • Working knowledge of identity and authentication architecture and data, including OAuth 2.0, OpenID Connect, single sign-on, role-based access, sessions or tokens, login events, and audit trails
- • Experience modeling application events, utilization, telemetry, and operational metrics and integrating operational data safely with analytical systems
- • Familiarity with modern full-stack architecture and effective collaboration with React/TypeScript developers and backend engineers; deep frontend implementation expertise is not required
- • Hands-on experience with CI/CD, automated database migrations, cloud platforms, managed databases and caches, infrastructure as code, monitoring, tracing, incident management, and Git-based review workflows
- • Demonstrated architecture and ownership mindset, with accountability for production data services and explicit technical tradeoffs
- • Ability to move between architecture, hands-on implementation, design and code review, troubleshooting, and operational support
- • Ability to work effectively across Application Engineering, Data Engineering, Platform, Security, Product, and operational teams
- • Strong communication and documentation skills, with a pragmatic bias toward automation, measurable reliability, incremental delivery, and maintainability
- • Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
- • Senior-level experience in DataOps, Platform Engineering, Database Engineering, Backend Engineering, Application Data Engineering, Data Engineering, or a closely related role
- • Demonstrated experience supporting production applications or data services with meaningful availability, security, performance, and recovery expectations
- • Experience with event-driven systems, message queues or streaming, change data capture, and asynchronous workflow patterns
- • Experience with containers, API gateways, serverless or managed application platforms, and distributed-system observability
- • Experience supporting applications that handle sensitive or regulated data and require strong auditability and access control
- • Experience connecting operational application data to cloud warehouses, lakehouses, semantic layers, business intelligence, or product analytics
Company Overview