Key Responsibilities

Architect and implement secure, scalable data platforms within the Databricks Lakehouse ecosystem.
Design and enforce enterprise data governance, security, and compliance frameworks.
Develop strategies for protecting sensitive data through data classification, masking, encryption, and access controls.
Implement and manage Role-Based Access Control (RBAC) and data access governance policies.
Build and optimize high-performance data pipelines, applications, and analytics solutions.
Design interoperable data architectures utilizing Delta Lake, Apache Iceberg, and modern data platform technologies.
Integrate Databricks with cloud-based data platforms and enterprise data ecosystems.
Create dashboards, monitoring frameworks, and operational reporting solutions.
Support AI/ML initiatives by enabling governed, secure, and self-service data access.
Optimize platform performance through query tuning, caching strategies, and workload management.
Collaborate with engineering, security, governance, and business stakeholders to establish best practices and architecture standards.

Required Qualifications

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
10+ years of experience in Data Engineering, Data Architecture, Data Security, or related disciplines.
Strong hands-on experience with the Databricks Lakehouse Platform.
Proven expertise in data governance, data security, and sensitive data management, including PHI/PII protection.
Experience implementing data masking, encryption, data classification, and access control strategies.
Strong knowledge of Delta Lake, Apache Iceberg, and modern data lakehouse architectures.
Experience working with cloud platforms, preferably Microsoft Azure.
Experience integrating Databricks with Snowflake or similar enterprise data platforms.
Proficiency in Python, SQL, Spark, and PySpark/Scala.
Experience building scalable data applications, APIs, and enterprise data solutions.
Strong understanding of performance optimization, query tuning, and platform scalability.
Excellent communication, analytical, and stakeholder management skills.

Experience supporting large-scale enterprise data modernization initiatives.
Exposure to AI/ML platforms and secure analytics environments.
Experience within regulated industries such as healthcare, life sciences, or financial services.
Databricks, Azure, Cloud, or Security-related certifications.
Knowledge of enterprise metadata management and data governance tools