Relanto
We are seeking a highly skilled and experienced Data Modeler to join our data engineering team. The ideal candidate will bring deep expertise in designing scalable and efficient data models for cloud platforms, particularly with a strong background in Oracle Data Warehousing and Databricks Lakehouse architecture. You will play a critical role in our strategic migration from an on-prem Oracle data warehouse to a modern cloud-based Databricks platform.
Key Responsibilities:
Design and implement conceptual, logical, and physical data models that support business requirements and analytics needs. Lead the migration of data models from an on-prem Oracle Data Warehouse to Databricks on AWS or Azure cloud. Reverse-engineer and analyze complex Oracle schemas, PL/SQL code, and existing structures to build equivalent or improved models on the cloud. Collaborate with data architects and engineers to define optimal data structures in Databricks, leveraging Delta Lake and Spark. Optimize data models for performance, scalability, and cost-efficiency in a cloud-native environment. Develop and maintain dimensional models using star and snowflake schemas to support BI and reporting tools. Ensure data governance standards are met by incorporating metadata management, data lineage, and documentation practices. Participate in data architecture reviews and provide input on best practices in modeling and data pipeline integration. Work closely with cross-functional teams to understand business needs and translate them into effective data solutions. Required Skills & Experience:
5+ years of hands-on experience in data modeling, including conceptual, logical, and physical design. Proven experience migrating large-scale Oracle DWH environments to Databricks Lakehouse or similar platforms. Strong expertise in Oracle database schemas, PL/SQL, and performance tuning. Proficiency in Databricks, Delta Lake, Spark SQL, and DataFrame APIs. Experience designing models optimized for cloud platforms (preferably AWS or Azure). Deep knowledge of dimensional modeling techniques (Star/Snowflake). Familiarity with tools and practices for metadata management, data lineage, and governance. Strong analytical and communication skills with the ability to work collaboratively in Agile teams. Ability to document and communicate data model designs to both technical and non-technical stakeholders.
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Design and implement conceptual, logical, and physical data models that support business requirements and analytics needs. Lead the migration of data models from an on-prem Oracle Data Warehouse to Databricks on AWS or Azure cloud. Reverse-engineer and analyze complex Oracle schemas, PL/SQL code, and existing structures to build equivalent or improved models on the cloud. Collaborate with data architects and engineers to define optimal data structures in Databricks, leveraging Delta Lake and Spark. Optimize data models for performance, scalability, and cost-efficiency in a cloud-native environment. Develop and maintain dimensional models using star and snowflake schemas to support BI and reporting tools. Ensure data governance standards are met by incorporating metadata management, data lineage, and documentation practices. Participate in data architecture reviews and provide input on best practices in modeling and data pipeline integration. Work closely with cross-functional teams to understand business needs and translate them into effective data solutions. Required Skills & Experience:
5+ years of hands-on experience in data modeling, including conceptual, logical, and physical design. Proven experience migrating large-scale Oracle DWH environments to Databricks Lakehouse or similar platforms. Strong expertise in Oracle database schemas, PL/SQL, and performance tuning. Proficiency in Databricks, Delta Lake, Spark SQL, and DataFrame APIs. Experience designing models optimized for cloud platforms (preferably AWS or Azure). Deep knowledge of dimensional modeling techniques (Star/Snowflake). Familiarity with tools and practices for metadata management, data lineage, and governance. Strong analytical and communication skills with the ability to work collaboratively in Agile teams. Ability to document and communicate data model designs to both technical and non-technical stakeholders.
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