Data Architect (Governance & Security)

Data Engineering Governance & Data Security

Experience:

Role Summary

Architect Data Engineering Governance & Security

Key Responsibilities

• Establish, enforce, and monitor Data Engineering governance standards, best practices, and guidelines across enterprise and self-service environments.
• Develop and maintain documentation for data engineering processes
• Define and implement data security policies and role-based access controls (RBAC/ABAC) across all data engineering processes.
• Oversee data classification, lifecycle management, and comprehensive metadata management to ensure transparency, traceability, and compliance.
• Implement and manage robust change control processes for all data engineering activities
• Monitor, maintain, and optimize data pipelines and workflows, ensuring reliability, scalability, and efficiency.
• Continuously monitor and optimize the performance of data engineering processes and resource utilization.
• Promote a culture of data engineering governance, data security awareness, and operational excellence within the team and across the organization.

Proficiency:

• Data Security

• Defining and implementing data security policies
• Security & role-based access control (RBAC/ABAC)

• Data Engineering Governance

• Focuses on the practices and policies that ensure the effective management of data engineering processes:
• Establishing and enforcing governance standards, best practices, and guidelines
• Data classification and lifecycle management

• Documentation and Metadata Management

• Maintaining comprehensive documentation and metadata for data engineering processes to track data lineage, understand data transformations, and ensure transparency

• Change Control Process

• Implementing and maintaining effective change control for data engineering activities

• Performance Monitoring and Optimization

• Continuously monitoring and optimizing data engineering process performance and resource utilization

• Data Pipeline Management

• Ensuring data pipelines are reliable, efficient, and scalable, including monitoring, maintenance, and workflow optimization

Technical Skills

• Databricks Lakehouse Platform:

• Medallion Architecture
• Unity Catalog (data governance, lineage)
• Delta Lake & DLT Pipelines
• PySpark Workbooks
• Spark SQL & SQL Warehouse

• Programming:

  Python, SQL, PySpark

• AWS Cloud Services:

  IAM, S3, Lambda, EMR, Redshift, Bedrock

• Other:

• Familiarity with DevOps and CI/CD processes
• Experience with any Data Security tool

Education

• Bachelors or Masters degree in Computer Science, Information Systems, Data Engineering, or a related field (Master’s preferred).

Preferred Qualifications

• Certifications in Databricks, AWS, or Data Security platforms.
• Experience working in regulated industries (e.g., finance, healthcare, manufacturing).
• Strong communication and documentation skills.
• Experience with dynamic data masking, data privacy, and compliance frameworks.