Senior Integration Analyst

The US Oncology Network•Dallas, TX

4d•Remote

About The Position

The US Oncology Network is looking for a Senior Integration Analyst to join our team at Texas Oncology. This is a remote role and requires the candidate to reside within Texas. As a part of The US Oncology Network, Texas Oncology delivers high-quality, evidence-based care to patients close to home. Texas Oncology is the largest community oncology provider in the country and has approximately 600+ providers in 280+ sites across Texas, our founders pioneered community-based cancer care because they believed in making the best available cancer care accessible to all communities, allowing people to fight cancer at home with the critical support of family and friends nearby. Our mission is still the same today—at Texas Oncology, we use leading-edge technology and research to deliver high-quality, evidence-based cancer care to help our patients achieve “More breakthroughs. More victories.” ® in their fight against cancer. Today, Texas Oncology treats half of all Texans diagnosed with cancer on an annual basis. The US Oncology Network is one of the nation’s largest networks of community-based oncology physicians dedicated to advancing cancer care in America. The US Oncology Network is supported by McKesson Corporation focused on empowering a vibrant and sustainable community patient care delivery system to advance the science, technology, and quality of care. What does the Senior Integration Analyst do? Including but not limited to The Senior Integration Analyst is a highly senior individual contributor accountable for the definition, integrity, and quality of enterprise integrations across Texas Oncology’s digital, clinical, and data ecosystem. This role owns integration solution intent across complex, multi initiative environments, ensuring system to system integrations are architecturally sound, scalable, secure, and aligned to long term interoperability, automation, and AI enablement goals. Operating at the intersection of clinical workflows, enterprise applications, data movement, and emerging AI enabled capabilities, this role serves as a central authority for integration definition, bridging business and clinical stakeholders, Solution Architects, engineering teams, data platforms, and analytics/AI initiatives. The Senior Integration Analyst proactively eliminates ambiguity, resolves cross system definition gaps, and ensures integration designs support high quality data, reliable operations, and future AI use cases. This role works independently, influences upstream decisions, and ensures integration quality across multiple concurrent initiatives.

Requirements

Bachelor’s degree in Information Systems, Computer Science, Engineering, or a related field.
5+ years of experience in an Integration Analyst, Systems Analyst, or senior technical analysis role.
Strong understanding of integration concepts, including APIs, interfaces, message flows, and data exchange patterns.
Demonstrated experience translating complex business and clinical workflows into technical integration requirements.
Experience working with data‑driven systems where data quality, consistency, and lineage are critical.
Excellent analytical, documentation, and stakeholder communication skills.
Proven ability to work independently across multiple initiatives.

Nice To Haves

Healthcare IT experience, particularly with clinical, revenue, or operational systems.
Familiarity with integration platforms, middleware, or interoperability frameworks.
Exposure to analytics, automation, or AI‑enabled solutions, including understanding data requirements that support them.
Experience supporting integrations involving data platforms, reporting, or machine learning pipelines.
Solid understanding of SDLC, including how integrations move from design through testing and production.

Responsibilities

Own end‑to‑end analysis and definition of system‑to‑system integrations across multiple concurrent initiatives, platforms, and domains.
Lead deep analysis of business and clinical workflows to identify integration needs, risks, failure points, and downstream data or automation impacts.
Translate complex business and clinical problems into complete, unambiguous integration requirements
Proactively identify and resolve integration scope gaps, conflicting system assumptions, and definition ambiguities that could compromise delivery, data quality, or AI outcomes.
Partner closely with Solution Architects to ensure integration requirements align with approved architecture patterns, enterprise standards, and AI readiness principles.
Collaborate with data and analytics teams to ensure integrations support trusted data pipelines required for reporting, analytics, and AI use cases.
Clarify integration constraints, assumptions, dependencies, and data limitations that may impact automation or AI outcomes.
Review integration designs and implementation plans to validate alignment with documented requirements and downstream AI/data consumers.
Support testing and validation by defining test scenarios, representative data conditions, and acceptance criteria, including data completeness and accuracy checks.
Serve as a senior analytical resource during integration issue triage, including data quality or AI‑impacting defects.
Define integration test scenarios, representative data conditions, and acceptance criteria, including validation of data completeness, accuracy, and consistency.
Serve as a senior analytical escalation point during integration defects, data quality issues, or AI‑impacting incidents.
Ensure integrations are designed for operational reliability, observability, and long‑term maintainability.
Engage directly with clinical, operational, technical, and analytics stakeholders to uncover integration breakdowns, manual workarounds, and data inconsistencies.
Translate fluently between business language (“what’s not working”), technical language (“what must integrate and how”), and data/AI language (“what data is required and why”).
Influence stakeholders toward standardized, scalable, and AI‑ready integration approaches, even in the presence of competing priorities.
Ensure integration documentation meets IT governance, security, privacy, audit, and responsible AI requirements.
Maintain traceability between business requirements, integration designs, delivered interfaces, and AI/data consumers.
Support responsible use of AI by ensuring integrations:
Provide explainable and auditable data flows
Avoid inappropriate data exposure or reuse
Align with privacy and regulatory expectations
Contribute to the continuous improvement of integration analysis templates, standards, and AI‑readiness checklists across IT.