Data Operations Manager

Overview Overview:   HealthEdge is looking for a Data Operations Manager with a focus on data insights who thinks like an engineer, works like a scientist and communicates like a strategist. This hybrid role sits at the intersection of data platform engineering and ITSM-aligned Problem Management – purpose-built to expose the hidden toil, technical debt and repeat failure patterns that drain engineering capacity and impact payer customer availability across our integrated platform of solutions.  You will leverage AI – including Claude and its supporting functions – to ingest disparate operational data sources, model failure trends and surface prioritized insights that drive permanent resolution rather than perpetual remediation.  WHY THIS ROLE EXISTS   Production stability is a business imperative for health plans that depend on HealthEdge solutions to process claims, manage care and engage members. Repeat incidents, unresolved technical debt and untracked toil create invisible drag on engineering velocity – and visible risk to client uptime. This role exists to make the invisible visible, and to turn data-driven findings into engineering action.  KEY RESPONSIBILITIES   Operational Data Engineering   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 consuming disproportionate remediation cycles.  Maintain data quality, lineage and governance standards across all ingested sources – ensuring 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.  Problem Management Practice   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 embed problem-driven prioritization into sprint planning and tech debt roadmaps.  Facilitate Problem Review sessions – leading cross-functional teams from data to decision  Define and track KPIs that demonstrate Problem Management value: reduction in repeat SEV1/SEV2 incidents, MTTR improvement, tech debt resolution velocity and engineering hours reclaimed from toil.  AI-Driven Insight & Visualization   Build interactive, executive-ready dashboards and data visualizations that make hotspots, failure modes and technical debt load immediately comprehensible to both engineering and business stakeholders.  Apply generative AI tooling to synthesize multi-source operational signals into clear, narrative-driven analysis – reducing time from data to decision.  Develop automated reporting workflows that surface trending issues and emerging risk patterns without requiring manual aggregation cycles.  Support monthly ceremonies by providing KPI and Outcome trending, highlighting influences to trending themes.  Stakeholder Communication & Business Translation   Present operational intelligence findings and Problem Management outcomes to Engineering leadership, VP-level+ audiences and cross-functional stakeholders.  Influence from a strategic perspective where the most urgent pockets of risk to platform availability exist, and drive prioritization accordingly.  Translate technical findings – infrastructure failure modes, code regression patterns, dependency risks – into business value framing that drives prioritization conversations.  Author Problem Record summaries, trend analyses and executive briefings that are concise, evidence-based and action-oriented.  REQUIRED QUALIFICATIONS   Technical Skills   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 a strong plus.  Operational & ITSM Knowledge   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 these to prioritize reliability work.  Communication & Leadership   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; you pursue root causes with the same rigor you bring to your data models.  PREFERRED QUALIFICATIONS   Experience in a healthcare SaaS environment or regulated platform with high availability requirements.  Prior role embedded