- Define and implement data quality rules, validation checks, and metrics for critical business domains.

- Develop Data Quality ('DQ') monitoring pipelines and alerts that proactively detect data issues.

- Conduct regular data profiling and quality assessments to identify gaps, inconsistencies, duplicates, and other data anomalies.

- Design and maintain data quality dashboards and reports that provide visibility into trends and issues.

- Leverage generative AI to automate workflows, enhance data quality, and support responsible prompt usage.

- Collaborate with data owners, stewards, and technical teams to triage and resolve data quality issues.

- Develop and document standard operating procedures (SOPs) for issue management, escalation, and stewardship workflows.

- Support root cause analysis (RCA) for recurring or high-impact data quality problems.

- Define and monitor key data quality KPIs, and support continuous improvement through insights and retrospective analysis.

- Evaluate and recommend data quality, testing, or observability tools that scale with the enterprise.

- Provide recommendations for improving data processes, governance practices, and quality standards.

- Ensure compliance with internal data governance policies, privacy standards, and audit requirements.

- Be familiar with corporate security policies and follow the guidance set out by processes and procedures .

Qualifications :

- 2 - 5 years of experience in a data quality, data analyst, or similar role.

- A degree in Computer Science, Information Systems, Data Science, or a related field is typically required.

- Strong understanding of data quality principles.

- Proficiency in SQL, Git Hub, R, Python, SQL Server and experience with BI tools such as Tableau, Power BI, or Sigma.

- Experience with cloud data platforms (e.g., Snowflake, BigQuery) and data transformation tools (e.g., dbt).

- Experience with Graph database.

- Exposure to GenAI tools and prompt-based automation

- Ability to build and interpret dashboards and communicate data quality findings to both technical and non-technical audiences.

- Understanding of data governance frameworks and regulatory considerations (e.g., PII handling), is an asset.

- Strong problem-solving skills and attention to detail.

- Familiarity with agile work environments and scrum ceremonies.

- Experience working with cross-functional teams to resolve data issues and align on standards.

- Excellent verbal and written communication skill