Data Quality (Data Management and Governance)

Position - Data Quality (Data Management and Governance)

Location - Plano, TX

Experience - 7+ Years

Type - W2 Contract

Job Description - Responsible for designing, implementing and operationalizing scalable Data Quality controls across enterprise data platforms. The role will support data profiling, rule applicability assessment, DQ rule development, automation, validation, certification, metadata integration and exception management.

Required Skills

Strong experience in Data Quality implementation and Data Quality frameworks.

Hands-on knowledge of one or more Data Quality / Data Observability tools, such as BigEye

Working knowledge of metadata/catalog/governance tools.

Strong SQL skills.

Working knowledge of Python for analysis and automation.

Experience working with REST APIs and platform integration.

Strong understanding of Data Quality dimensions and rule-design patterns.

Experience with data profiling and metadata-driven rule development.

Understanding of ETL/ELT and modern data architectures.

Experience with source-to-target mappings and reconciliation.

Strong analytical and troubleshooting skills.

Nice to have skills

CDMP certification preferred.

Experience with enterprise data governance and metadata management.

Exposure to regulated or highly governed data environments.

CI/CD and configuration-as-code experience.

Experience integrating DQ with catalog, lineage and workflow platforms.

Key Responsibilities

Establish and prioritize the Data Quality scope using available enterprise metadata and governance information.

Perform data profiling across structured enterprise data assets.

Assess applicability of key DQ dimensions including Completeness, Timeliness, Uniqueness, Validity and Accuracy.

Design reusable DQ rule patterns and rule libraries.

Develop simple, conditional and cross-source DQ rules.

Translate business Data Quality requirements into executable technical controls.

Develop source-to-target reconciliation and Accuracy checks.

Automate rule generation, deployment and monitoring using APIs and scripting.

Baseline DQ results and support threshold calibration.

Capture rule execution evidence including tested, passed and failed records where required.

Integrate DQ results with enterprise metadata/catalog platforms.

Support DQ certification and trust-status publication.

Integrate DQ failures with defect/exception-management processes.

Support integration of DQ controls with ETL/ELT and data-pipeline frameworks.

Maintain reusable technical specifications, operating procedures and implementation standards.