Data Engineer V - US

Rackspace Technology

14h • $165,830 - $243,142

About The Position

The Data Engineer V leads the technical direction, delivery standards, and day-to-day execution of a data engineering pod spanning core enterprise data warehouse pipelines and AI/RAG data infrastructure. The role translates stakeholder needs into a prioritized roadmap, mentors engineers, and owns operational accountability for the team's pipelines.

The position focuses on healthcare data and requires extensive healthcare-domain experience, including work with EMR/EHR systems such as Epic and/or Cerner. The Data Engineer V partners with healthcare providers, clinical operations teams, and healthcare organizations to translate clinical and provider needs into scalable data solutions. The role also sets team standards for data architecture, engineering practices, AI/RAG pipelines, security, quality, and operational performance.

Rackspace Technology describes itself as an operator of enterprise AI infrastructure and services, serving customers in more than 120 countries. Compensation varies by geographic market, job-related knowledge, skills, and experience. The stated range is $165,830.00 to $243,141.80 per year; the package may also include annual bonus or incentives, equity awards, and an Employee Stock Purchase Plan.

Requirements

• U.S. citizenship is required.
• Extensive experience in the healthcare domain.
• Experience with EMR/EHR systems, specifically Epic and/or Cerner.
• Experience partnering with healthcare providers, clinical operations teams, or healthcare organizations.
• Bachelor's degree in Computer Science, a related technical field, or equivalent hands-on experience.
• 8–12 years of experience in technology, with significant data-centric leadership.
• 6–8 years of hands-on experience architecting data solutions on a cloud platform.
• At least 2 years of formal experience leading engineers or technical workstreams.
• Expert-level SQL, data modeling, and semantic-layer design.
• Experience setting standards for schema and pipeline design patterns.
• Ability to set the team's approach to batch and streaming architecture, including Spark, Airflow, Databricks, or Snowflake, and CI/CD practices.
• Ability to direct and mentor engineers, manage workload prioritization, and deliver work on time.
• Ability to translate stakeholder needs and business ambiguity into technical plans and communicate with stakeholders.
• Ability to coordinate with adjacent technical teams and enforce security and privacy standards.
• Experience with healthcare data standards and compliance frameworks such as HIPAA, HL7, and FHIR.

Nice To Haves

• Cloud Professional Data Engineer or ML Engineer certification.
• Experience setting standards for LLM/RAG data architecture at scale.
• Experience with healthcare data compliance frameworks such as HIPAA, HL7, and FHIR.

Responsibilities

• Lead and mentor a team or pod of engineers, prioritize workload, and drive on-time Agile delivery.
• Own the team's source-management, version-control, and QA/testing standards, including standards for AI-assisted code contributions.
• Define and track the team's metrics strategy; own SLA/SLO tracking for pipelines and lead back-to-green plans when KPIs are missed.
• Track, analyze, and report pipeline uptime, MTTR, and data-quality KPIs to leadership.
• Own root-cause analysis and mitigation for complex pipeline and data-quality issues within the team's scope.
• Serve as a primary technical liaison to business and clinical stakeholders, translating needs into the team's roadmap for pipelines, data products, and AI/RAG capabilities.
• Contribute cost and budget input for the team's cloud and AI/vector-database usage.
• Own team documentation, SOPs, and escalation pathways.
• Participate in and help coordinate the on-call rotation, taking escalation ownership when issues arise.
• Set technical direction and standards for the team's data architecture, schema and pipeline design, batch/streaming patterns, CI/CD practices, and AI/RAG data pipelines.
• Coordinate handoffs with data science, security, and compliance teams to ensure platform readiness.
• Enforce security and privacy standards, including AI-specific risk considerations, across the team's work.
• Translate business ambiguity into actionable technical plans and present regularly to stakeholders and audiences of 10 or more.

Benefits

• Annual bonus or incentives may be included in the compensation package.
• Equity awards may be included in the compensation package.
• Employee Stock Purchase Plan (ESPP).
• Rackspace indicates that additional benefits information is available, but specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior