Data Analyst V

TX-HHSC-DSHS-DFPS•Austin, TX

1d•$7,000 - $8,560•Hybrid

About The Position

The Data Analyst V reports to the Data Management (DM) Unit Director within Office of Data, Analytics, and Performance (DAP), and performs highly complex (senior-level) data analysis, data research, data integration, visualization, and analytical solution development. The position supports the DAP’s mission to provide reliable, high-quality, secure, well-governed, and accessible data assets that enable HHSC programs and leadership to make data-informed decisions and improve program performance. The Data Analyst V serves as a senior technical and analytical resource supporting enterprise data warehouses, data marts, and cloud data platforms, with emphasis on Snowflake, Informatica/IICS, SQL, SAS Viya, Tableau, and related data visualization and analytical technologies. The position acquires, integrates, cleanses, transforms, validates, and analyzes complex data from multiple sources; identifies trends, patterns, anomalies, and opportunities for program and operational improvement; and translates data into meaningful reports, dashboards, visualizations, and actionable insights. The position develops and enhances data integration and ETL/ELT processes, analytical datasets, data-quality controls, reporting standards, dashboards, and reusable analytical solutions. It collaborates with business programs, data governance, IT, vendors, and other DAP teams to improve data availability, usability, quality, lineage, and analytical capabilities. The Data Analyst V also develops standards, procedures, technical documentation, and training materials; evaluates emerging analytical technologies; mentors staff; and promotes automation and analytical best practices. Works under limited supervision with moderate latitude for initiative and independent judgment.

Requirements

Knowledge of advanced data analysis, data research, statistical analysis, data interpretation, and analytical methodologies.
Knowledge of relational databases, cloud data platforms, enterprise data warehouses, data marts, and analytical data environments.
Knowledge of Snowflake architecture, databases, schemas, tables, views, stages, tasks, streams, stored procedures, security roles, and performance optimization concepts.
Knowledge of ETL/ELT principles, data integration patterns, data pipelines, and workflow development using Informatica PowerCenter, Informatica Intelligent Cloud Services (IICS), or comparable tools.
Knowledge of advanced SQL, including complex queries, joins, subqueries, common table expressions, window functions, stored procedures, query optimization, and data validation.
Knowledge of data visualization, dashboard design, KPI development, reporting standards, and visual communication best practices using Tableau, SAS Viya, or comparable tools.
Knowledge of statistical programming, exploratory data analysis, trend analysis, anomaly detection, and descriptive and inferential statistical methods.
Knowledge of data profiling, cleansing, standardization, reconciliation, validation, and data-quality measurement.
Knowledge of data governance principles, including metadata management, data lineage, data stewardship, data ownership, data classification, and data lifecycle management.
Knowledge of logical and physical data models, dimensional modeling, database design, and analytical dataset development.
Knowledge of automation methods and programming or scripting languages such as Python, SAS, SQL, or comparable technologies.
Knowledge of information security, role-based access, confidentiality, privacy, and protection of sensitive health and human services data.
Knowledge of federal and state laws, regulations, and policies applicable to data management, including HIPAA and HHSC security, privacy, records-management, and data-governance requirements.
Knowledge of systems development lifecycle, change management, testing, version control, incident management, and production implementation practices.
Knowledge of technical documentation standards, standard operating procedures, data dictionaries, business rules, and training-material development.
Knowledge of emerging developments and best practices in cloud computing, data engineering, analytics, artificial intelligence, automation, and business intelligence.
Skill in using Snowflake to query, integrate, transform, organize, and optimize large and complex datasets for analysis and reporting.
Skill in developing and maintaining ETL/ELT workflows using Informatica PowerCenter, IICS, or comparable data-integration technologies.
Skill in writing, reviewing, troubleshooting, and optimizing advanced SQL queries.
Skill in using SAS Viya, Tableau, or comparable analytical and visualization tools to analyze data and develop dashboards, reports, and visualizations.
Skill in designing dashboards that present accurate, meaningful, accessible, and actionable information to program, technical, and executive audiences.
Skill in collecting, acquiring, profiling, cleaning, standardizing, integrating, validating, and reconciling data from multiple sources.
Skill in conducting complex data analysis to identify trends, patterns, relationships, anomalies, risks, and opportunities for operational improvement.
Skill in developing data-quality measures, validation rules, exception reports, scorecards, and monitoring processes.
Skill in translating business questions and program objectives into data requirements, metrics, KPIs, analytical methods, and technical solutions.
Skill in developing analysis-ready datasets, semantic layers, reusable data products, and optimized structures supporting analytics and dashboards.
Skill in troubleshooting data pipelines, analytical applications, dashboard calculations, data discrepancies, and performance problems.
Skill in automating repetitive data acquisition, validation, reporting, monitoring, and documentation processes.
Skill in using statistical, programming, and scripting tools such as SAS, Python, SQL, or comparable technologies.
Skill in data modeling, source-to-target mapping, metadata documentation, and data-lineage analysis.
Skill in testing and validating ETL workflows, analytical datasets, business rules, statistical results, dashboards, and reports.
Skill in developing clear technical documentation, standard operating procedures, data dictionaries, workflow diagrams, user guides, and training materials.
Skill in presenting analytical findings, technical issues, risks, and recommendations clearly to technical and nontechnical audiences.
Skill in consulting, stakeholder engagement, requirements gathering, facilitation, collaboration, and customer service.
Skill in organizing and prioritizing multiple complex assignments, projects, deliverables, and competing deadlines.
Skill in mentoring staff, reviewing technical work, sharing knowledge, and supporting cross-training.
Skill in using sound judgment, critical thinking, root-cause analysis, and structured problem-solving.
Ability to analyze large, complex, and diverse datasets and convert the results into meaningful findings, recommendations, and actionable insights.
Ability to independently determine appropriate data sources, tools, methodologies, analytical approaches, and visualization techniques.
Ability to design, develop, and maintain scalable and reusable data-integration, analytics, reporting, and dashboard solutions.
Ability to detect and investigate data trends, anomalies, inconsistencies, redundancies, and potential data-quality concerns.
Ability to evaluate data accuracy, completeness, consistency, validity, timeliness, uniqueness, and reliability.
Ability to interpret business requirements and translate them into technical specifications, analytical datasets, measures, reports, and dashboards.
Ability to combine data from Snowflake, SQL Server, Oracle, flat files, and other structured or semi-structured sources.
Ability to explain complex data, statistical findings, and technical concepts clearly to program staff, executives, data professionals, and other stakeholders.
Ability to collaborate effectively with data owners, stewards, program areas, IT teams, governance teams, vendors, and executive leadership.
Ability to establish and apply consistent data, analytical, visualization, documentation, and reporting standards.
Ability to assess existing processes and recommend automation, standardization, performance, and efficiency improvements.
Ability to evaluate technical designs and vendor deliverables for alignment with business requirements, architectural standards, data quality, and governance expectations.
Ability to maintain confidentiality and appropriately handle protected health information, personally identifiable information, and other sensitive data.
Ability to develop and maintain metadata, data lineage, data dictionaries, business rules, and technical documentation.
Ability to plan, coordinate, and complete highly complex assignments under limited supervision and within established deadlines.
Ability to adapt to changing priorities, technologies, business needs, and regulatory requirements.
Ability to exercise initiative, independent judgment, and sound decision-making.
Ability to provide technical guidance, mentor team members, review work products, and promote knowledge sharing.
Ability to identify risks, communicate issues promptly, and recommend practical corrective actions.
Ability to maintain effective working relationships and provide responsive, professional support to internal and external customers.
Data Analysis Experience: Experience conducting complex data analysis, data research, data profiling, trend analysis, or statistical analysis using large and/or complex datasets.
SQL Skills: Experience developing and executing complex SQL queries for data extraction, transformation, validation, analysis, and reporting.
Database/Data Platform Experience: Experience working with relational databases, enterprise data warehouses, data marts, or cloud data platforms such as Snowflake, SQL Server, Oracle, or comparable technologies.
ETL/Data Integration: Experience developing, maintaining, troubleshooting, or supporting ETL/ELT processes and data pipelines using Informatica/IICS or comparable data integration technologies.
Data Visualization/BI: Experience developing reports, dashboards, visualizations, or other analytical products using Tableau, SAS Viya, Power BI, or comparable BI/analytics tools.
Data Quality: Experience performing data profiling, validation, cleansing, reconciliation, or identifying and resolving data-quality issues.
Documentation & Communication: Experience documenting analytical processes, data workflows, business/technical requirements, methodologies, SOPs, or technical procedures and communicating analytical findings to stakeholders.
Education/Equivalent Experience: Graduation from an accredited four-year college or university with major coursework in data analytics, statistics, computer science, information systems, mathematics, public health, business analytics. May substitute direct work experience in a technical field on a year-for-year basis in lieu of education for up to four years.

Responsibilities

Designs, develops, maintains, and enhances data integration and ETL/ELT solutions supporting DAP data warehouses, data marts, analytics, and reporting. Uses Snowflake, Informatica/IICS, SQL, and related technologies to acquire, transform, integrate, and prepare data from multiple complex sources. Develops reusable analytical datasets and optimized data structures to support downstream analytics and visualization. Troubleshoots data pipeline issues, identifies opportunities for automation and performance improvement, and collaborates with DM staff, IT, vendors, and business stakeholders on integration of new data sources. Supports metadata, data lineage, technical standards, and documentation associated with data pipelines and analytical datasets. (25%)
Conducts highly complex data research and analysis using SQL, SAS Viya, Snowflake, Tableau, and other analytical tools to identify trends, patterns, relationships, anomalies, and emerging issues. Performs data profiling, statistical and exploratory analysis, and comparative and trend analyses to support HHSC program and executive decision-making. Translates complex data into meaningful findings, recommendations, and actionable insights and explores opportunities for program efficiencies and performance improvement. Consults with internal customers to identify analytical requirements and determines appropriate data sources, methodologies, and analytical approaches. (25%)
Develops, implements, and monitors data-quality measures and analytical validation processes to assess data accuracy, completeness, consistency, validity, and reliability. Identifies data gaps, anomalies, inconsistencies, redundancies, and quality issues and collaborates with data owners, stewards, governance teams, IT, and vendors to identify root causes and implement corrective actions. Defines and maintains data and reporting standards and supports metadata, lineage, stewardship, and governance requirements. Develops standardized approaches for analytical datasets, measures, calculations, and reporting to improve consistency across DAP. (20%)
Designs, develops, enhances, and maintains interactive dashboards, reports, visualizations, and analytical products using Tableau, SAS Viya, and other approved business intelligence tools. Works with stakeholders to translate business questions and program objectives into meaningful metrics, KPIs, visualizations, and dashboard requirements. Develops underlying analytical datasets and optimized Snowflake queries to support efficient dashboard performance. Applies visualization and analytical best practices to communicate complex information clearly to technical, program, and executive audiences. Validates dashboard calculations and results and establishes consistent reporting definitions and standards. (20%)
Serves as a senior analytical and technical resource for the DM Unit. Develops and maintains standard operating procedures, technical documentation, data dictionaries, analytical methodologies, data workflows, reporting standards, and user guidance. Provides technical guidance and mentoring to DM Unit and DAP staff in the use of analytical datasets, Snowflake, Informatica, SAS Viya, Tableau, SQL, dashboards, and related tools. Evaluates emerging data, cloud, analytics, automation, and visualization technologies and recommends solutions that improve efficiency, scalability, data quality, and analytical capabilities. Promotes reusable solutions, automation, knowledge sharing, cross-training, and continuous improvement throughout the DM Unit. (10%)