Job Summary:
We are seeking a Data Governance Lead to join our UAE Technology Offshoring Team. The Ideal candidate will be responsible for the below:
Data Modelling with a strong understanding of data warehouse (DWH) concepts, data issues’ validation, and data mesh architecture and Star Schema
The ideal candidate should have hands-on experience with cloud platforms like Azure and Databricks, as well as data catalogue tools.
They should also be proficient in data quality tools and familiar with basic data engineering concepts, including data pipelines, ETL processes, and data lineage tracking.
Additionally, this role requires strong English and communication skills, as it involves working with diverse cultures and nationalities.
The candidate should be comfortable collaborating with cross-functional teams in an agile environment, documenting processes, and engaging with stakeholders across different domains.
Key Responsibilities:
1. Data Modelling Management:
- Implement and maintain data governance frameworks, policies, and standards.
- Ensure high-quality data management across DWH models and data sources.
- Develop and execute data quality rules, validation processes, and monitoring frameworks.
- Identify and resolve data quality issues in collaboration with business and operation teams.
- Work with data quality tools to automate data validation and cleansing processes.
2. Data Warehouse (DWH) & Data Modelling:
- Support the design and governance of data warehouse (DWH) models based on business requirements.
- Ensure data consistency and integrity across structured and semi-structured datasets.
- Collaborate with data architects and engineers to optimize DWH performance.
- Work with Data Mesh architecture to enable decentralized data ownership and governance.
3. Cloud & Data Engineering Concepts:
- Utilize Databricks for Quality & Analytics.
- Understand ETL (Extract, Transform, Load) and ELT concepts for data ingestion pipelines.
- Support the development and governance of data pipelines using tools like Databricks or Apache Spark.
- Implement data lineage tracking to ensure data transparency and traceability across pipelines.
4. Data Privacy & Compliance:
- Identification, classification, and protection of PII and sensitive data.
- Ensure compliance with data privacy regulations such as GDPR, CCPA, and local policies.
- Maintain data lineage and governance documentation.
5. Agile & Squad-Based Collaboration:
- Work in an agile environment using the squad model.
- Collaborate with cross-functional teams, including data engineers, analysts, and business stakeholders.
- Participate in sprint planning, backlog grooming, and agile ceremonies.
6. Communication & Cross-Cultural Collaboration:
- Work with teams across different cultures and nationalities to align data governance strategies.
- Clearly communicate technical and governance concepts to both technical and non-technical stakeholders.
- Facilitate discussions, workshops, and training sessions to promote data governance best practices.
Required Skills & Qualifications:
- Experience in data governance, data quality, and data modelling.
- Technical Expertise:
- Strong knowledge of data governance frameworks and data quality management.
- Experience with data warehouse (DWH) concepts and Data Mesh architecture.
- Knowledge of SQL, Python, or Spark
- Hand-on experience in IBM Tools
- Experience working with data quality tools
- Understanding of basic data engineering concepts, including ETL processes, data pipelines, and data lineage tracking.
- Experience with data catalog tools ( Azure Purview).
- Hands - on experience in Erwin
- Data Privacy & Compliance:
Understanding of PII identification, data classification, and regulatory compliance.
- Agile & Squad Methodology: Experience working in an agile environment with cross-functional squads.
- Strong English & Communication Skills:
- Strong verbal and written communication skills to engage with technical and business stakeholders.
