Data Operations Manager

HealthEdge•Remote,

5d•$130,000 - $140,000•Remote

About The Position

HealthEdge is seeking a Data Operations Manager with a focus on data insights, blending engineering, scientific, and strategic thinking. This hybrid role operates at the intersection of data platform engineering and ITSM-aligned Problem Management. Its purpose is to identify and address hidden toil, technical debt, and recurring failure patterns that consume engineering resources and affect the availability of our integrated platform solutions for payer customers. The role will utilize AI, including Claude and its associated functions, to process diverse operational data, model failure trends, and deliver prioritized insights aimed at achieving permanent resolutions rather than continuous remediation. Production stability is critical for health plans relying on HealthEdge solutions for claims processing, care management, and member engagement. Persistent incidents, unresolved technical debt, and unaddressed toil impede engineering efficiency and pose a significant risk to client uptime. This position is designed to make these hidden issues visible and translate data-driven findings into actionable engineering initiatives.

Requirements

5+ years of data engineering experience with production-grade pipeline design, transformation logic, and operational data modeling.
Proficiency with Python or Scala for data processing.
Strong SQL for analytical querying against large, event-driven datasets.
Hands-on experience with Jira and at least two of the following: PagerDuty, Datadog, Splunk, ServiceNow – ideally in an operational analytics or SRE context.
Experience integrating large language model (LLM) APIs – including Anthropic Claude, OpenAI, or similar – into data workflows, automated summarization pipelines, or insight generation applications.
Proficiency building interactive dashboards and data visualizations; Amazon Quick Suite is a strong plus.
Working knowledge of ITIL or equivalent ITSM frameworks, specifically Incident Management, Problem Management, and Change Management process disciplines.
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 (toil quantification, error budgets, SLO/SLA measurement) and how engineering teams use them to prioritize reliability work.
Strong written and verbal communication skills, with demonstrated experience presenting technical analysis to VP or C-level audiences.
Ability to translate complex, multi-variable findings into business impact narratives that drive prioritization decisions.
Comfortable driving cross-functional alignment, navigating competing priorities across Engineering, Product, Operations, and Leadership stakeholders.
Self-directed and intellectually curious; pursue root causes with the same rigor applied to data models.

Nice To Haves

Experience in a healthcare SaaS environment or regulated platform with high availability requirements.
Prior role embedded in an SRE, NOC, Platform Engineering, or Operations function, particularly one with formal Problem Management or post-incident review responsibilities.
Experience building AI-powered operational tooling (e.g., automated incident summarization, intelligent alert correlation, 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 to consolidate data from PagerDuty, Jira, ServiceNow, Datadog, Splunk, and other operational sources into a unified analytical layer.
Develop and curate data models to identify repeat incidents, known error patterns, chronic alert noise, and engineering toil that consumes excessive remediation time.
Maintain data quality, lineage, and governance standards across all ingested sources to ensure findings are defensible when presented to senior leadership.
Leverage AI and automation, including the Claude API and Claude-powered workflows, to accelerate pattern detection, root cause hypothesis generation, and report synthesis across large operational datasets.
Own and drive the Problem Management lifecycle across HealthEdge client-facing products.
Translate incident patterns into structured Problem Records with defined scope, impact quantification, and recommended permanent fix strategies.
Partner with Engineering, SRE, Platform, and Product teams to integrate problem-driven prioritization into sprint planning and tech debt roadmaps.
Facilitate Problem Review sessions, leading cross-functional teams from data analysis to decision-making.
Define and track KPIs to demonstrate Problem Management value, such as reduction in repeat SEV1/SEV2 incidents, MTTR improvement, tech debt resolution velocity, and engineering hours reclaimed from toil.
Build interactive, executive-ready dashboards and data visualizations to make hotspots, failure modes, and technical debt load easily understandable to both engineering and business stakeholders.
Apply generative AI tooling to synthesize multi-source operational signals into clear, narrative-driven analyses, reducing the time from data to decision.
Develop automated reporting workflows to surface trending issues and emerging risk patterns without manual aggregation.
Support monthly ceremonies by providing KPI and outcome trending, highlighting influences on trending themes.
Present operational intelligence findings and Problem Management outcomes to Engineering leadership, VP-level+ audiences, and cross-functional stakeholders.
Influence strategic prioritization by identifying the most critical risks to platform availability and driving action accordingly.
Translate technical findings (infrastructure failure modes, code regression patterns, dependency risks) into business value narratives that support prioritization discussions.
Author concise, evidence-based, and action-oriented Problem Record summaries, trend analyses, and executive briefings.