Data Governance Engineer

Position : DataGovernance

Experience : 5-8 years

Location : Hyderabad

Mode of work : 5 days

Mode of Interview : 2 Rounds (Virtual, F2F)

Notice Period : Immediate-15 days

JD :

1. Data Quality :

- Define and Measure Data Quality Metrics : Establish metrics for accuracy, completeness, validity, consistency, timeliness, and reliability.

- Continuous Monitoring and Remediation : Regularly monitor data quality, conduct audits, perform root cause analysis for recurring data issues, and implement preventive measures and remediation plans.

- Data Profiling : Develop and maintain comprehensive data profiles to understand data characteristics.

- Data Validation : Create and implement validation rules to ensure that incoming data conforms to expected formats and values.

- Data Cleansing : Design and execute data cleansing processes to correct errors and inconsistencies, enhancing overall data quality and reliability.

2. Data Governance :

- Establish Governance Framework : Implement and enforce data governance practices to ensure compliance with regulatory requirements and corporate policies, ensuring data is managed according to best practices.

- Metadata Management : Develop and maintain a comprehensive metadata repository to document data definitions, lineage, and usage, ensuring it is kept up to date and accessible to end users.

- Understand User Needs : Collaborate with business users to identify data needs, pain points, and requirements, ensuring the data is fit for its intended use.

- Identify Improvement Areas : Continuously seek opportunities for process improvement in data governance and quality management.

- User Roles and Access Requirements : Understand user roles and access requirements for systems, so that similar protection can be implemented into the analytical solutions.

- Row-Level Security : Work with the data & analytics team to establish row-level security for analytical solutions, ensuring data is accessible only to authorised users.

3. Continuous Improvement :

- Establish Naming Conventions : Define business friendly table names and column names, along with synonyms, to ensure data easily accessible using AI.

- Create Synonyms : Implement synonyms to simplify data access and enhance data readability.

- Establish KPIs for data governance and quality efforts and create regular reports for stakeholders to track progress and demonstrate the value of data governance initiatives.

- Continuous Improvement : Establish a feedback loop where users can report data quality issues and suggest improvements.