Design and maintain enterprise data architecture that aligns with business strategy and technology roadmaps. Define logical, conceptual, and physical data models across operational and analytical platforms. Develop scalable architecture patterns for data lakes, data warehouses, lakehouses, and modern analytics platforms. Establish standards for metadata, lineage, master data, reference data, and data lifecycle management. Drive technology evaluations and architectural decisions for cloud-based data platforms.
Design, build, and optimize enterprise data platforms using cloud-native technologies. Develop and maintain scalable ETL/ELT pipelines for structured and semi-structured data. Implement infrastructure automation and deployment pipelines using Infrastructure as Code and CI/CD practices. Optimize platform performance, reliability, scalability, and cost across cloud environments. Troubleshoot platform issues and continuously improve operational resilience.
Design and implement enterprise data governance frameworks. Lead initiatives involving confidential data identification, classification, masking, tokenization, encryption, and access control. Define and implement data retention, auditing, and compliance standards. Collaborate with Security, Risk, Legal, and Compliance teams to ensure adherence to regulatory requirements. Establish enterprise metadata management and data lineage capabilities.
Develop enterprise data quality frameworks including validation, reconciliation, completeness, accuracy, and monitoring. Implement automated quality controls within ingestion and transformation pipelines. Define KPIs and metrics to measure platform health and data quality. Lead initiatives that improve trust in enterprise data assets.
Administer enterprise analytics platforms including Databricks, Microsoft Fabric, Snowflake, Azure and AWS environments Manage workspace provisioning, Unity Catalog governance, compute optimization, storage management, and platform security. Implement role-based access controls and least-privilege security models. Monitor platform utilization, capacity planning, and cost optimization.
Partner with BI teams to enable trusted enterprise reporting. Support semantic models, reporting platforms, and self-service analytics capabilities. Ensure data products are discoverable, reusable, and governed.
Lead architecture reviews and technical design sessions. Provide technical leadership to engineering teams and mentor junior architects and engineers. Define enterprise best practices, standards, and reusable design patterns. Review solution designs for scalability, maintainability, security, and performance.
Improve platform reliability through monitoring, automation, observability, and incident response. Develop operational runbooks and disaster recovery strategies. Drive continuous improvement initiatives across platform operations. Support production deployments and release management.
Partner with Finance, HR, Operations, Product, Security, and Engineering teams to understand business requirements. Translate business objectives into scalable technical solutions. Communicate architecture decisions and technical strategy to both technical and executive audiences. Lead cross-functional initiatives involving multiple business units and external vendors.
Bachelor's degree in Computer Science/ Information Systems/Engineering, or related field. 5+ years of experience in Data Engineering, Data Architecture, Platform Engineering, or related disciplines. Extensive experience designing enterprise-scale cloud data platforms. Strong understanding of modern data architecture principles including Lakehouse, Data Mesh, and Data Fabric concepts. Experience implementing enterprise data governance and security frameworks. Proven ability to lead large-scale technical initiatives from design through production.
Cloud Platforms: Microsoft Azure (Preferred), AWS Data Platforms: Databricks (Required), Microsoft Fabric, Snowflake, Azure Synapse Analytics, Azure Data Lake Storage Databases: SQL Server, Oracle, PostgreSQL, Azure SQL, Delta Lake Data Engineering: Spark, PySpark, SQL, Python, ETL/ELT pipeline development, Streaming and batch processing Governance & Metadata: Microsoft Purview, Unity Catalog, Data Catalogs, Data Lineage, Data Classification, Data Masking, Data Quality Frameworks DevOps & Automation: Git, Azure DevOps, GitHub Actions, Terraform, ARM/Bicep, CI/CD Pipelines Reporting & Analytics: Power BI, Semantic Models, DAX, Data Modeling
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