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Specialized Databricks expertise and seniority limit applicants, but consultancy demand yields medium competition.
Strong Databricks, lakehouse, and data architecture focus requires domain-specific experience, limiting cross-industry transferability.
Strict years and Databricks/Spark expertise requirement makes shortlisting highly selective.
Lead design and architecture of scalable, governed data solutions leveraging lakehouse and warehouse patterns to support enterprise analytics and AI.
Oversee cloud modernization and migration initiatives specifically involving Azure Databricks and Databricks Lakehouse.
Ensure delivery of secure, cost-effective cloud data platforms meeting scalability, performance, governance, and cost goals while mentoring cross-functional teams.
12-22 years of senior IT experience with leadership in data architecture, solution architecture, or data engineering.
Expert-level skills in Databricks, Apache Spark, Delta Lake, and advanced SQL on modern data platforms.
Hands-on experience with at least one major cloud platform: Azure, AWS, or GCP for cloud-based data solutions.
Work Experience Required: 12-22 years; Notice Period: Not explicitly mentioned in the JD.
Experienced architect with strong background in building production-ready, scalable, secure data architectures using modern data patterns (lakehouse, data warehouse, lambda, kappa).
Proven ability to lead and mentor multidisciplinary teams spanning data engineering, analytics, cloud, and DevOps in cloud modernization and migration projects, particularly on Databricks.
Skilled in aligning technical architecture with business requirements and compliance needs, demonstrating advanced stakeholder management and communication abilities.