Data Quality Engineer

Ost•Orlando, FL

About The Position

Optimal Solutions & Technologies (OST, Inc.) is focused on excellence. We specialize in providing Management Consulting, Information Technology, and Research Development and Engineering services. The fundamental distinction of the OST team is its business knowledge in both the public and private sectors. We serve the aerospace & transportation, association & nonprofit, defense, education, energy, financial, healthcare, and technology & telecommunications industries. OST is successful because we listen to our clients, we learn from our clients, and we know our clients. Sr. Data Quality Engineer Description of specific duties in a typical workday for this position: The Sr. Data Quality Engineer supports Government programs by ensuring the accuracy, completeness, consistency, and reliability of data used for decision-making, system integration, and enterprise operations. The individual designs, implements, and maintains data quality frameworks, validation rules, and monitoring processes to support trusted data across enterprise systems and platforms. Responsibilities include defining data quality standards and metrics; developing automated data validation and profiling routines; identifying, analyzing, and resolving data quality issues; and supporting root cause analysis and corrective actions. The Sr. Data Quality Engineer collaborates with data architects, systems engineers, developers, and program stakeholders to ensure data quality requirements are integrated into system designs, interfaces, and workflows. Additional duties include supporting data governance activities; documenting data quality processes and results; producing data quality reports and dashboards; supporting testing and validation of data during system integration and transformation efforts; and ensuring compliance with applicable data standards and policies. The role supports continuous improvement of data processes to enhance enterprise analytics, reporting, and operational effectiveness.

Requirements

Bachelor's degree in data science, Computer Science, Information Systems, Engineering, Mathematics, Statistics, or a related technical field
Minimum of 10 years of experience supporting data quality, data engineering, data analytics, or enterprise data management within a DoD, Federal, or large enterprise environment
Demonstrated experience with: Data quality assessment, validation, and profiling Data integration and transformation processes Development of data quality metrics and dashboards Data analysis and issue resolution Collaboration across technical and business stakeholders
Active Secret security clearance

Nice To Haves

Experience supporting enterprise transformation, data modernization, or analytics programs
Familiarity with: Data governance frameworks ETL/ELT processes and tools SQL, Python, or similar data analysis tools Data quality or master data management tools
Certifications or training in: Data management or data engineering Agile or DevSecOps environments

Responsibilities

ensuring the accuracy, completeness, consistency, and reliability of data used for decision-making, system integration, and enterprise operations
designs, implements, and maintains data quality frameworks, validation rules, and monitoring processes to support trusted data across enterprise systems and platforms
defining data quality standards and metrics
developing automated data validation and profiling routines
identifying, analyzing, and resolving data quality issues
supporting root cause analysis and corrective actions
collaborates with data architects, systems engineers, developers, and program stakeholders to ensure data quality requirements are integrated into system designs, interfaces, and workflows
supporting data governance activities
documenting data quality processes and results
producing data quality reports and dashboards
supporting testing and validation of data during system integration and transformation efforts
ensuring compliance with applicable data standards and policies
supporting continuous improvement of data processes to enhance enterprise analytics, reporting, and operational effectiveness