Senior Data Analyst, Quality & Risk

Job Description

Job Description:\n\n \n \n \n \n \n \n \n \n The Senior Analyst, Data Analytics is an experienced individual contributor responsible for leading complex data, reporting, business intelligence, and analytics initiatives that support operational, financial, regulatory, clinical, compliance, and strategic decision-making across Imperial Health Plan. The Senior Analyst serves as a technical and analytical subject-matter resource, independently manages high-impact assignments, and leads projects from requirements definition through implementation, validation, stakeholder adoption, and ongoing support. This position partners directly with department leaders, business owners, subject-matter experts, Information Technology, and other cross-functional stakeholders to define business problems, develop analytical strategies, determine appropriate data and technical approaches, and translate findings into actionable recommendations. The Senior Analyst is expected to exercise a high degree of independent judgment, manage multiple priorities, identify and mitigate project risks, and provide guidance or mentoring to Analysts and other team members. The role requires advanced hands-on experience with traditional relational SQL environments and practical experience with Databricks or a comparable cloud/lakehouse platform to support Imperial’s planned migration. Advanced proficiency in SQL, Python, and Power BI is required. SAS, R, statistical analysis, and healthcare payer or managed-care data experience are highly valued. \n \n Essential Functions and Responsibilities\n 1. Advanced Business Analysis and Analytical Consulting \n  Lead consultations with business owners, department leaders, and subject-matter experts to define complex business, reporting, and analytical needs. \n  Translate broad, ambiguous, or cross-functional business questions into structured analytical requirements, technical specifications, success measures, and implementation plans. \n  Independently evaluate alternative analytical approaches and recommend solutions based on business impact, data availability, technical feasibility, risk, scalability, and organizational priorities.\n  Serve as an analytical consultant to stakeholders by interpreting findings, identifying implications, and recommending actions rather than only delivering data.\n  Identify conflicting requirements, data limitations, operational risks, and dependencies and facilitate resolution with appropriate stakeholders. \n  Develop analysis plans, outcome measures, and methodologies that support informed business decisions and measurable improvement. \n 2. Advanced Data Analytics and Technical Development\n  Design, develop, optimize, and maintain complex SQL queries, data transformations, reporting logic, analytical workflows, and reusable data assets. \n  Develop and support solutions in the current relational SQL/database environment and apply Databricks or comparable cloud/lakehouse expertise as the planned platform migration is implemented.  Use Python for advanced data preparation, automation, validation, workflow development, and analytical processing. \n  Develop and refine analytical or statistical models, metrics, and algorithms as appropriate to the business need; test, validate, and document results. \n  Perform complex data wrangling, profiling, cleansing, integration, reconciliation, and root-cause analysis across multiple enterprise data sources. \n  Evaluate data architecture, source-system behavior, and downstream dependencies when designing or modifying analytical solutions.\n  Apply sound analytical methods, including descriptive, correlational, inferential, or predictive techniques when appropriate.\n 3. Business Intelligence, Reporting, and Automation  Lead the design, development, implementation, and enhancement of complex operational, financial, regulatory, compliance, clinical, and executive reporting solutions.\n  Develop advanced Power BI dashboards, semantic models, measures, visualizations, and interactive reporting products.  Identify opportunities to automate, consolidate, redesign, or retire manual and duplicative reporting processes.\n  Establish scalable reporting approaches that improve consistency, performance, maintainability, and end-user experience. \n  Present complex analytical findings through clear reports, visualizations, presentations, and executive-level narratives that support decision-making. \n  Ensure reporting solutions are coherent across business definitions, data sources, logic, and organizational standards. 4. Project Leadership and Project Management\n  Lead small-to-medium data analytics, reporting, automation, migration, or business intelligence projects and serve as the analytical lead on larger cross-functional initiatives. \n  Lead assigned analytical workstreams supporting Imperial’s planned migration to Databricks, including data transformations, validation plans, reporting development, testing, implementation readiness, and transition to ongoing support; coordinate with the overall migration lead and Information Technology.\n  Define project scope, objectives, deliverables, milestones, dependencies, resource needs, risks, assumptions, and target completion dates. \n  Develop and maintain project plans, work breakdowns, status updates, decision logs, issue/risk logs, and other project documentation appropriate to the assignment.\n  Coordinate activities across Data Analytics, business stakeholders, Information Technology, vendors, and other departments to keep projects on schedule.\n  Facilitate project meetings, requirements sessions, design reviews, testing discussions, and implementation readiness activities. \n  Identify project risks and scope changes early, assess business and timeline impact, recommend mitigation strategies, and escalate material issues when necessary. \n  Manage multiple concurrent projects and priorities while maintaining visibility of status, ownership, deadlines, and outstanding decisions. \n  Support user acceptance testing, implementation planning, stakeholder communication, training, transition to production, and post-implementation review.\n  Apply practical project management methods to ensure accountability, documentation, stakeholder alignment, and successful delivery.\n 5. Data Quality, Validation, and Risk Management\n  Establish validation strategies and quality-control standards for complex analytical and reporting deliverables. \n  Review source data, business logic, calculations, joins, filters, assumptions, and output for reasonableness, completeness, and accuracy.\n  Lead investigation of complex discrepancies, data-quality issues, and unexpected analytical results across systems or business processes.\n  Assess the impact of data limitations or methodology changes and communicate material risks to management and stakeholders. \n  Determine when work is sufficiently validated for release and when additional review, clarification, or escalation is required.\n  Maintain appropriate documentation of assumptions, exclusions, methodology, definitions, limitations, and validation results. \n 6. Report, Product, and Solution Ownership \n  Serve as primary technical and analytical owner for complex or high-impact reports, dashboards, datasets, and analytical processes.\n  Manage assigned solutions throughout the lifecycle, including requirements, design, development, validation, deployment, maintenance, enhancement, and retirement.\n  Assess downstream impact before implementing changes to production reporting logic, data sources, calculations, or workflows. \n  Maintain current technical and business documentation, including data sources, logic, definitions, code locations, schedules, business ownership, and support procedures. \n  Promote appropriate version control, shared storage, documentation, backup ownership, and operational continuity. \n 7. Leadership, Mentoring, and Knowledge Sharing \n  Provide technical guidance, coaching, and occasional mentoring to Analysts and other team members. \n  Review analytical approaches, code, report logic, validation methods, and documentation and provide constructive feedback when appropriate. \n  Share expertise, reusable methods, standards, and lessons learned across the Data Analytics team.\n  Support cross-training and development of secondary ownership for critical reports and processes.\n  Model strong accountability, communication, documentation, analytical rigor, and customer service. \n  Influence stakeholders and peers through clear technical explanations, evidence-based recommendations, and collaborative problem-solving.\n 8. Continuous Improvement and Department Standards \n  Identify, recommend, and implement improvements to Data Analytics processes, development standards, intake practices, documentation, quality assurance, and reporting governance.\n  Evaluate new technologies, tools, and analytical methods that may improve department capability or efficiency.\n  Support data platform transitions, system implementations, migrations, and enterprise data initiatives.\n  Contribute to the development of reusable templates, coding standards, validation practices, and reporting methodologies. \n Minimum Qualifications\n  Bachelor's degree in Computer Science, Data Science, Data Analytics, Business Analytics, Information Systems, Statistics, Mathematics, Engineering, or a closely related quantitative/technical field. \n  Minimum three (3) years of progressively responsible professional experience in data analytics, reporting, business intelligence, report development, or a directly related field; experience should demonstrate increasing complexity, independence, and ownership. \n  Minimum three (3) years of hands-on experience using SQL and/or comparable analytical programming languages in a professional environment. \n  Demonstrated experience independently managing complex analytical assignments and delivering solutions to business stakeholders.\n  Demonstrated project leadership or project management experience, including requirements, planning, milestones, risks, stakeholder coordination, testing, implementation, and status communication. \n Preferred Qualifications \n  Master's degree in Computer Science, Data Science, Data Analytics, Business Analytics, Information Systems, Statistics, Mathematics, Engineering, or a related quantitative/technical field.\n  Healthcare industry experience is strongly preferred, particularly within a health plan, managed-care organization, payer, provider, or healthcare analytics/reporting environment. \n  Experience with healthcare claims, eligibility/membership, provider, pharmacy, finance, compliance, quality, regulatory, or operational data. \n  Experience leading cross-functional analytics, reporting, automation, data migration, or business intelligence projects.  Formal project management training or certification such as PMP, CAPM, Agile/Scrum, or equivalent is a plus. \n  Experience with SAS/SAS Enterprise Guide and/or R. \n  Experience with statistical modeling, machine learning, natural language processing, or big-data analytics is a plus.\n Required Technical Skills\n  Advanced SQL – complex queries, joins, subqueries, aggregations, transformations, performance considerations, data validation, and relational database concepts. \n  Python – data manipulation, transformation, automation, validation, and analytical workflows. \n  Power BI – advanced report/dashboard development, data modeling, measures, DAX, visualization, and stakeholder-facing BI solutions.\n  Databricks – practical experience developing or supporting analytical workflows in Databricks or a comparable cloud/lakehouse platform. \n  Traditional SQL/database environments – demonstrated professional experience working with enterprise relational databases. \n  Advanced Microsoft Excel and working proficiency with Word and PowerPoint. \n  Strong understanding of data quality, data governance, documentation, and analytical validation practices. Core Competencies\n  Advanced analytical and critical-thinking skills. \n  Strong project management, organization, prioritization, and follow-through. \n  Ability to independently manage complex assignments under general direction. \n  Ability to evaluate alternatives, make sound analytical decisions, and recommend solutions on matters of significance to assigned projects or business areas. \n  Strong stakeholder management and consulting skills.\n  Ability to communicate complex technical concepts and analytical findings to technical and nontechnical audiences. \n  Ability to influence decisions through evidence, analysis, technical expertise, and clear communication. \n  Strong attention to accuracy, detail, documentation, and risk identification. \n  Ability to mentor less-experienced analysts and contribute to team development.\n  Ability to manage competing deadlines and remain accountable for project outcomes. \n Decision-Making and Level of Independence\n The Senior Analyst operates with a high degree of independence within established organizational policies, department standards, and project objectives. The position is expected to determine appropriate analytical and technical approaches; define validation methods; recommend business and reporting solutions; prioritize assigned project activities; identify and mitigate risks; and determine when issues require management, legal, compliance, information technology, or other subject-matter escalation. Major policy decisions, significant budget commitments, and matters outside delegated authority are escalated to the Manager, Data Analytics or appropriate leadership. \n Performance Expectations\n  Deliver accurate, complete, timely, and well-documented analytical solutions.\n  Successfully lead assigned projects from initiation through implementation and transition to ongoing support. \n  Maintain clear visibility of project status, ownership, risks, dependencies, and target dates.\n  Demonstrate independent judgment, technical depth, and accountability appropriate to a senior individual contributor. \n  Provide actionable recommendations and comm