Job Description
Required Qualifications
12+ years of experience in data architecture, data warehousing, or analytics platform engineering.
Hands-on expertise with Azure Databricks, Delta Lake, and Unity Catalog.
Strong experience with Power BI semantic modeling, DAX optimization, and security models.
Deep knowledge of lakehouse, data warehouse, and distributed data processing architectures.
Proven experience implementing data governance and data quality frameworks at enterprise scale.
Strong understanding of cloud security, IAM, RBAC, and compliance controls.
Excellent communication skills with the ability to translate business requirements into technical architecture.
Preferred Qualifications
Experience with CI/CD tooling (Azure DevOps, GitHub Actions) for data platforms.
Familiarity with Azure-native services and enterprise cloud standards.
Experience supporting multi-tenant or multi-domain analytics platforms.
Background in retail, consumer goods, supply chain, or financial analytics.
Job Responsibilities
Architect and own Azure Databricks Lakehouse solutions using Medallion architecture (Bronze/Silver/Gold).
Define ingestion patterns using Fivetran, REST APIs, SFTP, CDC, and incremental load strategies.
Design canonical, dimensional, and semantic data models optimized for analytical workloads.
Establish Power BI semantic layer standards including datasets, DAX measures, calculation groups, and composite models.
Implement enterprise KPI governance, metric versioning, and certified dataset patterns.
Operationalize data governance frameworks covering data quality, metadata management, lineage, and stewardship.
Leverage Unity Catalog for centralized metadata, RBAC, row-level and object-level security, auditing, and lineage.
Define data quality rules, validation checks, reconciliation logic, and observability metrics.
Design CI/CD pipelines for Databricks notebooks, jobs, and Power BI artifacts across Dev/Test/Prod environments.
Optimize performance through partitioning, clustering, caching, and query optimization techniques.
Ensure compliance with enterprise data classification, privacy, retention, and regulatory standards.
Review and approve solution designs, data models, and transformation logic from engineering teams.
Mentor data engineers and analysts on architecture, modeling, governance, and best practices.
Establish and operationalize a Data Governance framework covering data ownership, stewardship, quality, metadata, lineage, and lifecycle management.
Define and enforce data standards, policies, and controls to ensure data consistency, trust, and regulatory compliance across the enterprise.
Leverage Unity Catalog to implement centralized governance for metadata management, access control, auditing, and data lineage.
Partner with business and technology stakeholders to drive KPI standardization, certified datasets, and data quality accountability.
Ensure governance is embedded into the data platform through automated controls, validation rules, and secure access patterns.
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