What are the responsibilities and job description for the Data Quality Engineering Lead position at Mudrasys?
Company Description Mudrasys is a global systems integrator and IT services company providing a broad range of technology solutions across multiple industries. The organization has built its reputation on delivering quality outcomes by using effective sourcing models and proven delivery frameworks. With a team of experienced software and business professionals, Mudrasys offers strong consulting expertise, solution development, and implementation capabilities. As one of the faster-growing firms in its space, the company provides opportunities to work on impactful client projects and modern technologies. Team members join a collaborative environment focused on measurable results and long-term client relationships.
Role Description The Data Quality Engineering Lead is a full-time, on-site role based in Beverly Hills, CA. This role is responsible for defining and implementing data quality standards, frameworks, and processes across multiple data platforms and business domains. The Data Quality Engineering Lead will work closely with data engineering, analytics, and business stakeholders to profile data, identify quality issues, and develop rules, checks, and remediation strategies. Daily responsibilities include designing automated data quality tests, building monitoring dashboards, analyzing anomalies, and coordinating root-cause analysis and corrective actions with technical teams. The role will also lead and mentor data quality engineers, establish best practices, and contribute to data governance initiatives to ensure reliable, trusted data for reporting, analytics, and operational systems.
Qualifications
Role Description The Data Quality Engineering Lead is a full-time, on-site role based in Beverly Hills, CA. This role is responsible for defining and implementing data quality standards, frameworks, and processes across multiple data platforms and business domains. The Data Quality Engineering Lead will work closely with data engineering, analytics, and business stakeholders to profile data, identify quality issues, and develop rules, checks, and remediation strategies. Daily responsibilities include designing automated data quality tests, building monitoring dashboards, analyzing anomalies, and coordinating root-cause analysis and corrective actions with technical teams. The role will also lead and mentor data quality engineers, establish best practices, and contribute to data governance initiatives to ensure reliable, trusted data for reporting, analytics, and operational systems.
Qualifications
- Strong experience in data quality engineering, including data profiling, data validation, and data cleansing across large and complex datasets.
- Hands-on expertise with relational databases, SQL, and ETL/ELT processes, including integration with data warehouses, data lakes, or modern cloud data platforms.
- Proven ability to design and implement automated data quality checks, test frameworks, and monitoring dashboards using industry-standard tools and technologies.
- Experience working with data governance, metadata management, and master data management concepts, policies, and standards.
- Demonstrated capability to lead and mentor technical team members, manage priorities, and drive cross-functional initiatives to completion.
- Strong analytical and problem-solving skills with the ability to translate business requirements into data quality rules and technical specifications.
- Excellent written and verbal communication skills to collaborate effectively with technical and non-technical stakeholders.
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field; advanced degree or relevant certifications in data management or quality are a plus.
- Experience in consulting, systems integration, or working in a client-facing environment is beneficial.
- Familiarity with software development life cycle (SDLC), agile methodologies, and best practices for documentation and change management.