Data Engineer V - US

Rackspace Technology

3d•$165,830 - $243,142•Remote

About The Position

Leads the technical direction, delivery standards, and day-to-day execution of a data engineering pod — spanning core EDW pipelines and AI/RAG data infrastructure — translating stakeholder needs into a prioritized roadmap while mentoring engineers and owning operational accountability for the team's pipelines.

Requirements

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 hands-on experience architecting data solutions on a cloud platform.
2+ years formally leading engineers or technical workstreams.
Expert-level SQL, data modeling, and semantic-layer design; sets standards for the team's schema/pipeline design patterns.
Sets the team's approach to batch/streaming architecture (Spark, Airflow, Databricks/Snowflake) and CI/CD practices.
Sets standards for the team's AI/RAG data pipelines (embedding strategy, vector-store selection, corpus curation) and AI-assisted development practices.
Directs and mentors a team of engineers; manages workload prioritization and on-time delivery.
Translates business ambiguity into actionable technical plans; regularly presents to stakeholders and audiences of 10+.
Coordinates handoffs with adjacent technical teams (data science, security, compliance) to ensure platform readiness.
Enforces security/privacy standards, including AI-specific risk considerations, across the team's work.

Nice To Haves

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

Responsibilities

Lead and mentor a team/pod of engineers, prioritizing workload and driving on-time, Agile delivery.
Own source-management, version-control, and QA/testing standards for the team, 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 KPIs (pipeline uptime, MTTR, data-quality metrics) to leadership.
Own root-cause analysis and mitigation for complex pipeline/data-quality issues within the team's scope.
Serve as a primary technical liaison to business/clinical stakeholders, translating needs into the team's roadmap (pipelines, data products, AI/RAG capabilities).
Contribute cost/budget input for the team's cloud and AI/vector-database usage.
Own documentation, SOPs, and escalation pathways for the team.
Participate in and help coordinate the on-call rotation, owning escalation ownership when issues arise.