Top Analysis Reporting Jobs in Dallas, TX

Develop and support Informatica ETL/MDM solutions (development ~70%, support ~30%). Implement error-handling frameworks with schedulers (Autosys/Control-M/Tidal), configure PowerCenter Grid, use IDQ and Informatica MDM/Cloud, perform performance tuning, Unix scripting, and Teradata/SQL-based data cleansing, profiling, and standardization.

Lead enterprise data management strategy, build an MDM function, govern data quality and metadata, integrate MDM with ERP/CRM, manage a centralized team, and drive stakeholder adoption and data literacy to support analytics and operations.

Seeking a SOX Controls Tester with expertise in ITGC and automation testing, focusing on SOX compliance and control testing procedures.

The Infra BA will support both business and technical teams, analyzing requirements, project managing, and creating essential documentation like BRDs. They will leverage technical skills, communication abilities, and problem-solving capabilities to enhance project delivery.

The Data Engineer will analyze, develop, implement, test, and support data warehouse projects. Responsibilities include project support, data validation, and enhancing data management processes, all while collaborating with team members and improving performance.

The Systems Analyst supports end-users, configures and tests systems, ensures quality through testing, and communicates changes effectively to stakeholders.

The Collibra Lead will define Data Governance Organization, develop a business glossary, lead workshops, ensure alignment with executive vision, and design technical architecture.

The job involves analyzing data to support business decisions and help improve profitability but lacks detailed responsibilities in the description.

The Lead Data Analyst will lead data analysis for public health projects, supervise analysts, and develop statistical models and reports.

Lead design, build, and operate scalable AWS-based data pipelines and real-time ingestion for analytics and ML. Implement data modeling, automated validation, observability, and event-driven architectures; mentor engineers and collaborate with Product and ML teams to productionize feature stores and ML-ready datasets.