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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cyber Managed Services - Data Analyst - IAM Operational Reporting - Senior - **Company:** Ernst & Young LLP - **Location:** Dallas, TX, United States - **Experience:** Expert - **Salary:** $92,900 - $153,900 - **Contract:** Permanent contract - **Skills:** Third Normal Form, Adaptable Database Systems, Agile Methodology, Artificial Intelligence, Business Analytics Applications, Data Analysis, Computing Platforms, Confluence, JIRA, Cyber Security, Information Systems, Databases, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Mapping, Data Transformation, Data Mining, Data Profiling, Data Visualization, Data Warehousing, Relational Databases, Database Queries, Decision Support Systems, Data Flow Control, R (Programming Language), Graph Database, Identity and Access Management, Systems Analysis, Python (Programming Language), Unix Shell, Microsoft SQL Server, Neo4j, Operational Data Store, Oracle (Applications), Scrum Methodology, Power BI, Requirements Management, PL-SQL, SQL Databases, Tableau (Software), Unstructured Data, Scripting, Data Ingestion, Snowflake, Documentation System, Information Technology, Data Lineage, Data Analytics, Data Management, Tools for Reporting, Physical Data Models, SailPoint, Data Pipelines, Servicenow - **Published:** November 14, 2026 - **Apply:** https://careers.ey.com/talentcommunity/apply/1457355433/?locale=en_US ## About the Role * Bachelor's degree in computer science, information technology, information systems, data analytics, or a related field, or equivalent practical experience. * Approximately 6-9 years of experience in data analysis, systems analysis, business analysis, data engineering support, or a related role. * Demonstrated experience with requirements elicitation, gap analysis, data mining, data profiling, source-to-target mapping, and data-quality assessment. * Strong understanding of dimensional, relational, and unstructured data-modeling concepts. * Experience developing conceptual, logical, and physical data models, including third normal form in relational database environments. * Strong SQL skills and experience with one or more scripting languages such as Python, R, Unix shell scripting, or PL/SQL. * Working knowledge of database and data-platform technologies such as Snowflake, Oracle, and Microsoft SQL Server. * Experience supporting data warehouses, operational data stores, ETL processes, data pipelines, or comparable data-integration solutions. * Demonstrated ability to combine complex data from multiple systems, identify meaningful trends, and translate findings into clear recommendations. * Experience developing dashboards and data visualizations using Tableau, Power BI, or comparable reporting tools. * Ability to communicate complex data concepts, issues, and recommendations to both technical and non-technical audiences. * Strong analytical, problem-solving, documentation, collaboration, and stakeholder-management skills. * Ability to operate effectively in a fast-paced Agile environment with changing business and technology priorities., * Experience working with identity and access management, identity governance, cybersecurity, technology risk, or access-control data. * Familiarity with IAM data domains such as identities, accounts, entitlements, applications, credentials, access activity, certifications, and lifecycle events. * Experience with SailPoint IdentityIQ or AccessHub and related access-governance reporting. * Experience with ServiceNow, Jira, Confluence, or comparable workflow and Agile-delivery platforms. * Experience with Snowflake and modern cloud-based data platforms. * Experience using data-modeling or lineage-documentation tools. * Familiarity with graph databases or analytics platforms such as Neo4j. * Experience in a regulated financial-services environment. * Familiarity with audit evidence, control reporting, data governance, and risk-remediation processes. Technology Environment * Data analysis and scripting: SQL, PL/SQL, Python, R, and Unix shell scripting. * Data platforms: Snowflake, Oracle, Microsoft SQL Server, relational databases, operational data stores, and enterprise data warehouses. ## Description This role will help consolidate identity and access data across enterprise systems, strengthen operational reporting, and provide timely insights into the client's access-risk posture. The analyst will engage with data-source owners, engineers, product owners, architects, and business partners to define data requirements, profile and validate source data, document lineage, and translate business needs into scalable data models and ingestion patterns. The role will support access-governance applications, enterprise controls, remediation activities, and data-driven decision-making., * Engage with IAM data-source owners and stakeholders to gather requirements, identify authoritative data, and define data needed to support risk-reduction objectives and access-governance processes. * Analyze business and technical requirements and translate them into conceptual, logical, and physical data models that address data quality, performance, lineage, and scalability. * Create, maintain, and scale repeatable data-ingestion patterns supporting operational reporting across thousands of enterprise systems. * Profile, map, reconcile, and validate identity, account, entitlement, credential, inventory, activity, and control-related data from multiple source systems. * Document source-to-target mappings, data lineage, transformation logic, control points, and data-flow diagrams. * Combine data from multiple sources to identify trends, patterns, anomalies, coverage gaps, and actionable IAM risk insights. * Define data-quality rules, metrics, and exception-handling processes; investigate data discrepancies and support remediation with source owners and engineering teams. * Write and interpret SQL and scripts to query, manipulate, reconcile, and profile data across cloud and on-premises platforms. * Support engineers in designing and validating ETL pipelines, data transformations, operational data-store structures, and reporting datasets. * Develop requirements, user stories, acceptance criteria, test scenarios, and supporting evidence for new data sources, reporting features, and control enhancements. * Create dashboards, reports, and visualizations that clearly communicate access-risk posture, control coverage, data quality, operational trends, and remediation priorities. * Participate in Agile ceremonies, backlog refinement, sprint planning, demonstrations, testing, release readiness, and continuous-improvement activities. * Maintain data dictionaries, requirements documentation, analytical findings, process flows, operating procedures, and stakeholder-ready materials. * Work independently through ambiguity while proactively seeking decisions, clarifications, and support when necessary., * Data integration: ETL pipelines, repeatable ingestion patterns, source-to-target mapping, data profiling, reconciliation, transformation, and lineage management. * Reporting and visualization: Tableau, Power BI, operational dashboards, control reporting, and data-quality metrics. * IAM ecosystem: SailPoint IdentityIQ / AccessHub and identity, account, entitlement, credential, inventory, activity, certification, and lifecycle data sources. * Delivery and collaboration: Jira, Confluence, ServiceNow, Agile delivery methods, documentation repositories, and cross-functional engineering processes. * Emerging analytics: Neo4j graph analytics and AI-assisted use cases for IAM insights, analysis, impact assessment, and remediation decision support. Success Measures * Accurate, timely, and scalable data-ingestion patterns supporting IAM operational reporting across in-scope systems. * Improved completeness, quality, traceability, and reliability of identity and access data. * Clear source-to-target mappings, lineage documentation, and data-quality controls. * Actionable dashboards and analyses that improve visibility into access risk, control coverage, operational trends, and remediation priorities. * Well-defined requirements, user stories, acceptance criteria, and test evidence that enable successful engineering delivery. * Timely resolution of data issues through effective collaboration with data-source owners, engineers, architects, product owners, and business partners. * Consistent delivery against sprint and release commitments while adhering to Fidelity security, data, architecture, and engineering standards. 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