Databricks Data Modeler

As a Databricks Data Modeler, your role involves designing, developing, and optimizing enterprise-scale data models and platforms on Databricks. You should have strong expertise in data modeling, data warehousing, ETL/ELT processes, and cloud-based data engineering solutions. Key Responsibilities: - Design and implement conceptual, logical, and physical data models for enterprise data platforms. - Develop scalable data solutions using Databricks, Apache Spark, and cloud technologies. - Create and maintain dimensional models (Star Schema, Snowflake Schema) for analytical and reporting requirements. - Collaborate with business stakeholders, data architects, and engineering teams to gather and analyze data requirements. - Build and optimize ETL/ELT pipelines for large-scale data processing. - Ensure data quality, consistency, governance, and security standards are maintained. - Perform data profiling, data lineage analysis, and impact assessments. - Optimize Databricks workloads for performance and cost efficiency. - Support data migration and modernization initiatives from legacy systems to cloud platforms. Qualifications: - Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field. - 5+ years of experience in Data Engineering/Data Modeling. - Strong communication and stakeholder management skills. Required Skills: - Strong hands-on experience with Databricks. - Expertise in Data Modeling (Conceptual, Logical, Physical). - Experience with Dimensional Modeling, Data Warehousing concepts, Star/Snowflake schemas. - Strong SQL development and query optimization skills. - Experience with Apache Spark, PySpark, and Databricks notebooks. - Knowledge of ETL/ELT frameworks and data integration tools. - Experience with cloud platforms such as Azure, AWS, or GCP. - Strong understanding of Data Governance, Data Quality, and Metadata Management. Good to Have: - Experience with Azure Data Factory (ADF), Delta Lake, and Lakehouse architecture. - Knowledge of Data Vault Modeling. - Familiarity with BI tools such as Power BI, Tableau, or Looker. - Experience with CI/CD and DevOps practices in data engineering. As a Databricks Data Modeler, your role involves designing, developing, and optimizing enterprise-scale data models and platforms on Databricks. You should have strong expertise in data modeling, data warehousing, ETL/ELT processes, and cloud-based data engineering solutions. Key Responsibilities: - Design and implement conceptual, logical, and physical data models for enterprise data platforms. - Develop scalable data solutions using Databricks, Apache Spark, and cloud technologies. - Create and maintain dimensional models (Star Schema, Snowflake Schema) for analytical and reporting requirements. - Collaborate with business stakeholders, data architects, and engineering teams to gather and analyze data requirements. - Build and optimize ETL/ELT pipelines for large-scale data processing. - Ensure data quality, consistency, governance, and security standards are maintained. - Perform data profiling, data lineage analysis, and impact assessments. - Optimize Databricks workloads for performance and cost efficiency. - Support data migration and modernization initiatives from legacy systems to cloud platforms. Qualifications: - Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field. - 5+ years of experience in Data Engineering/Data Modeling. - Strong communication and stakeholder management skills. Required Skills: - Strong hands-on experience with Databricks. - Expertise in Data Modeling (Conceptual, Logical, Physical). - Experience with Dimensional Modeling, Data Warehousing concepts, Star/Snowflake schemas. - Strong SQL development and query optimization skills. - Experience with Apache Spark, PySpark, and Databricks notebooks. - Knowledge of ETL/ELT frameworks and data integration tools. - Experience with cloud platforms such as Azure, AWS, or GCP. - Strong understanding of Data Governance, Data Quality, and Metadata Management. Good to Have: - Experience with Azure Data Factory (ADF), Delta Lake, and Lakehouse architecture. - Knowledge of Data Vault Modeling. - Familiarity with BI tools such as Power BI, Tableau, or Looker. - Experience with CI/CD and DevOps practices in data engineering.