Data Operations Manager

HealthEdge•Remote,

120d•$130,000 - $140,000•Remote

About The Position

HealthEdge is seeking a Data Operations Manager focused on data insights. The role combines data platform engineering with ITSM-aligned Problem Management to uncover operational toil, technical debt, and repeat failure patterns that affect engineering capacity and customer availability across HealthEdge’s integrated platform. The manager uses AI, including Claude and related functions, to ingest operational data, model failure trends, and produce prioritized insights that support permanent resolutions. The position exists to improve production stability for health plans that rely on HealthEdge solutions. It makes recurring incidents, unresolved technical debt, and untracked toil visible, then turns data-driven findings into engineering action. The role owns Problem Management across client-facing products, partners with Engineering, SRE, Platform, and Product teams, and communicates findings and outcomes to engineering leaders and executive stakeholders. The role is remote in the United States. The work environment may be hybrid or remote and involves working across time zones, extended periods sitting or standing at a computer, and possible travel depending on company needs. The position may also require occasional movement around an office. The annual U.S. base salary range is $130,000 to $140,000; final compensation depends on factors including skills, experience, qualifications, and education.

Requirements

At least 5 years of data engineering experience, including production-grade pipeline design, transformation logic, and operational data modeling.
Proficiency in Python or Scala for data processing and strong SQL skills for analytical queries against large, event-driven datasets.
Hands-on Jira experience and experience with at least two of PagerDuty, Datadog, Splunk, or ServiceNow; operational analytics or SRE context is preferred in this requirement.
Experience integrating large language model APIs, including Anthropic Claude, OpenAI, or similar, into data workflows, automated summarization pipelines, or insight-generation applications.
Proficiency building interactive dashboards and data visualizations.
Working knowledge of ITIL or equivalent ITSM frameworks, specifically Incident Management, Problem Management, and Change Management.
Demonstrated ability to identify repeat failure patterns in incident or monitoring data and drive structured root-cause analysis and resolution workflows.
Familiarity with SRE principles, including toil quantification, error budgets, and SLO/SLA measurement, and how engineering teams use them to prioritize reliability work.
Strong written and verbal communication skills, including demonstrated experience presenting technical analysis to VP- or C-level audiences.
Ability to translate complex, multivariable findings into business-impact narratives that drive prioritization decisions.
Ability to drive cross-functional alignment across Engineering, Product, Operations, and Leadership stakeholders with competing priorities.
Self-directed and intellectually curious, with rigor in pursuing root causes and developing data models.

Nice To Haves

Amazon Quick Suite experience.
Experience in a healthcare SaaS environment or regulated platform with high availability requirements.
Prior experience embedded in an SRE, NOC, Platform Engineering, or Operations function, particularly with formal Problem Management or post-incident review responsibilities.
Experience building AI-powered operational tooling, such as automated incident summarization, intelligent alert correlation, or AI-assisted root-cause classification.
Familiarity with HealthEdge products or the payer technology landscape.
ITIL Foundation certification or equivalent.

Responsibilities

Design, build, and maintain pipelines that consolidate data from PagerDuty, Jira, ServiceNow, Datadog, Splunk, and other operational sources into a unified analytical layer.
Develop and curate data models that identify repeat incidents, known error patterns, chronic alert noise, and engineering toil that consumes disproportionate remediation cycles.
Maintain data quality, lineage, and governance standards across ingested sources.
Use AI and automation, including Claude APIs and Claude-powered workflows, to accelerate pattern detection, root-cause hypothesis generation, and report synthesis.
Own and drive the Problem Management lifecycle across HealthEdge client-facing products.
Translate incident patterns into structured Problem Records with defined scope, quantified impact, and recommended permanent-fix strategies.
Partner with Engineering, SRE, Platform, and Product teams to incorporate problem-driven prioritization into sprint planning and technical-debt roadmaps.
Facilitate Problem Review sessions and lead cross-functional teams from data analysis to decisions.
Define and track Problem Management KPIs, including repeat SEV1/SEV2 incident reduction, MTTR improvement, technical-debt resolution velocity, and engineering hours reclaimed from toil.
Build interactive, executive-ready dashboards and visualizations that explain hotspots, failure modes, and technical-debt load to engineering and business stakeholders.
Use generative AI tools to synthesize operational signals into narrative-driven analysis and reduce the time from data to decision.
Develop automated reporting workflows that surface trending issues and emerging risk patterns without manual aggregation.
Support monthly ceremonies with KPI and outcome trends and highlight influences on trending themes.
Present operational intelligence findings and Problem Management outcomes to Engineering leadership, VP-level and more senior audiences, and cross-functional stakeholders.
Identify urgent risks to platform availability and influence prioritization accordingly.
Translate technical findings, including infrastructure failure modes, code regression patterns, and dependency risks, into business-value framing.
Write concise, evidence-based, action-oriented Problem Record summaries, trend analyses, and executive briefings.