- Data Architecture Design:
- Develop and maintain the overall data architecture blueprint, incorporating data warehousing, data lakehouse, and data modeling principles.
- Design and implement data solutions leveraging the medallion architecture (Bronze, Silver, Gold layers) for efficient data processing and quality management.
- Define data storage strategies, including the selection and configuration of appropriate Azure services such as Azure Data Lake Storage (ADLS) Gen2, Azure Synapse Analytics, Azure SQL Database, and NoSQL databases.
- Design scalable and high-performance data pipelines for batch and real-time data ingestion, transformation, and loading using Azure Data Factory (ADF) or Azure Databricks.
- Establish data governance frameworks, including data quality rules, metadata management, data lineage, and security policies using Azure Purview or similar tools.
- Define and implement data retention and archival strategies.
- Ensure compliance with relevant data regulations and security standards.
- Data Modeling:
- Develop conceptual, logical, and physical data models optimized for various use cases, including analytical reporting, business intelligence, and machine learning.
- Apply different data modeling techniques such as relational modeling (e.g., star schema, snowflake schema), dimensional modeling, and potentially Data Vault modeling based on requirements.
- Ensure data models are scalable, maintainable, and aligned with business needs.
- Azure Data Platform Expertise:
- Demonstrate deep expertise in a wide range of Azure data services, including but not limited to:
- Storage: Azure Data Lake Storage Gen2, Azure Blob Storage.
- Data Processing: Azure Data Factory, Azure Databricks (PySpark, Spark SQL), Azure Stream Analytics, Azure Synapse Pipelines.
- Data Warehousing: Azure Synapse Analytics (Dedicated SQL Pools, Serverless SQL Pools), Azure SQL Database.
- NoSQL Databases: Azure Cosmos DB.
- Data Governance: Azure Purview.
- Orchestration: Azure Logic Apps, Azure Functions.
- Monitoring: Azure Monitor, Azure Log Analytics.
- Architect and implement solutions for data integration across diverse data sources, both on-premises and in the cloud.
- Optimize Azure data services for performance, cost-efficiency, and scalability.
- Implement security best practices for Azure data services, including access control, encryption, and network security.
- Collaboration and Leadership:
- Collaborate effectively with cross-functional teams, including data engineers, data scientists, business analysts, and business stakeholders, to understand data requirements and deliver solutions.
- Provide technical guidance and mentorship to data engineers and other team members.
- Lead the evaluation and adoption of new Azure data technologies and services.
- Communicate complex technical concepts clearly and effectively to both technical and non-technical audiences.
- Participate in the development of data standards and best practices.
- Problem Solving and Innovation:
- Troubleshoot and resolve complex data-related issues.
- Stay up-to-date with the latest trends and advancements in data architecture, data management, and cloud technologies.
- Identify opportunities for innovation and improvement in our data platform and processes.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.
- Minimum of 8 years of experience in data warehousing, data modeling, and data architecture.
- Minimum of 4 years of hands-on experience designing and implementing data solutions on Microsoft Azure.
- Strong understanding of data warehousing concepts, principles, and best practices.
- In-depth knowledge of data lakehouse architectures and their benefits.
- Proven experience in implementing the medallion architecture for data management.
- Excellent data modeling skills, including conceptual, logical, and physical model design.
- Proficiency in SQL and experience with various database systems (relational and NoSQL).
- Hands-on experience with Azure data services such as ADLS Gen2, ADF, Azure Databricks, Azure Synapse Analytics, Azure SQL Database, and Azure Purview.
- Experience with data integration tools and techniques.
- Strong understanding of data governance, data quality, and data security principles.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work independently and collaboratively in a fast-paced environment.
Preferred Qualifications:
- Azure Data Engineer Associate or Azure Solutions Architect Expert certification.
- Experience with big data technologies (e.g., Hadoop, Spark).
- Familiarity with data science and machine learning workflows.
- Experience with DevOps practices and CI/CD pipelines for data solutions.
- Knowledge of Python or other programming languages relevant to data engineering.
- Experience with data visualization tools (e.g., Power BI, Tableau).
Requirements
- Data Architecture Design:
- Develop and maintain the overall data architecture blueprint, incorporating data warehousing, data lakehouse, and data modeling principles.
- Design and implement data solutions leveraging the medallion architecture (Bronze, Silver, Gold layers) for efficient data processing and quality management.
- Define data storage strategies, including the selection and configuration of appropriate Azure services such as Azure Data Lake Storage (ADLS) Gen2, Azure Synapse Analytics, Azure SQL Database, and NoSQL databases.
- Design scalable and high-performance data pipelines for batch and real-time data ingestion, transformation, and loading using Azure Data Factory (ADF) or Azure Databricks.
- Establish data governance frameworks, including data quality rules, metadata management, data lineage, and security policies using Azure Purview or similar tools.
- Define and implement data retention and archival strategies.
- Ensure compliance with relevant data regulations and security standards.
- Data Modeling:
- Develop conceptual, logical, and physical data models optimized for various use cases, including analytical reporting, business intelligence, and machine learning.
- Apply different data modeling techniques such as relational modeling (e.g., star schema, snowflake schema), dimensional modeling, and potentially Data Vault modeling based on requirements.
- Ensure data models are scalable, maintainable, and aligned with business needs.
- Azure Data Platform Expertise:
- Demonstrate deep expertise in a wide range of Azure data services, including but not limited to:
- Storage: Azure Data Lake Storage Gen2, Azure Blob Storage.
- Data Processing: Azure Data Factory, Azure Databricks (PySpark, Spark SQL), Azure Stream Analytics, Azure Synapse Pipelines.
- Data Warehousing: Azure Synapse Analytics (Dedicated SQL Pools, Serverless SQL Pools), Azure SQL Database.
- NoSQL Databases: Azure Cosmos DB.
- Data Governance: Azure Purview.
- Orchestration: Azure Logic Apps, Azure Functions.
- Monitoring: Azure Monitor, Azure Log Analytics.
- Architect and implement solutions for data integration across diverse data sources, both on-premises and in the cloud.
- Optimize Azure data services for performance, cost-efficiency, and scalability.
- Implement security best practices for Azure data services, including access control, encryption, and network security.
- Collaboration and Leadership:
- Collaborate effectively with cross-functional teams, including data engineers, data scientists, business analysts, and business stakeholders, to understand data requirements and deliver solutions.
- Provide technical guidance and mentorship to data engineers and other team members.
- Lead the evaluation and adoption of new Azure data technologies and services.
- Communicate complex technical concepts clearly and effectively to both technical and non-technical audiences.
- Participate in the development of data standards and best practices.
- Problem Solving and Innovation:
- Troubleshoot and resolve complex data-related issues.
- Stay up-to-date with the latest trends and advancements in data architecture, data management, and cloud technologies.
- Identify opportunities for innovation and improvement in our data platform and processes.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.
- Minimum of 8 years of experience in data warehousing, data modeling, and data architecture.
- Minimum of 4 years of hands-on experience designing and implementing data solutions on Microsoft Azure.
- Strong understanding of data warehousing concepts, principles, and best practices.
- In-depth knowledge of data lakehouse architectures and their benefits.
- Proven experience in implementing the medallion architecture for data management.
- Excellent data modeling skills, including conceptual, logical, and physical model design.
- Proficiency in SQL and experience with various database systems (relational and NoSQL).
- Hands-on experience with Azure data services such as ADLS Gen2, ADF, Azure Databricks, Azure Synapse Analytics, Azure SQL Database, and Azure Purview.
- Experience with data integration tools and techniques.
- Strong understanding of data governance, data quality, and data security principles.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work independently and collaboratively in a fast-paced environment.
Preferred Qualifications:
- Azure Data Engineer Associate or Azure Solutions Architect Expert certification.
- Experience with big data technologies (e.g., Hadoop, Spark).
- Familiarity with data science and machine learning workflows.
- Experience with DevOps practices and CI/CD pipelines for data solutions.
- Knowledge of Python or other programming languages relevant to data engineering.
- Experience with data visualization tools (e.g., Power BI, Tableau).
Benefits
- Data Architecture Design:
- Develop and maintain the overall data architecture blueprint, incorporating data warehousing, data lakehouse, and data modeling principles.
- Design and implement data solutions leveraging the medallion architecture (Bronze, Silver, Gold layers) for efficient data processing and quality management.
- Define data storage strategies, including the selection and configuration of appropriate Azure services such as Azure Data Lake Storage (ADLS) Gen2, Azure Synapse Analytics, Azure SQL Database, and NoSQL databases.
- Design scalable and high-performance data pipelines for batch and real-time data ingestion, transformation, and loading using Azure Data Factory (ADF) or Azure Databricks.
- Establish data governance frameworks, including data quality rules, metadata management, data lineage, and security policies using Azure Purview or similar tools.
- Define and implement data retention and archival strategies.
- Ensure compliance with relevant data regulations and security standards.
- Data Modeling:
- Develop conceptual, logical, and physical data models optimized for various use cases, including analytical reporting, business intelligence, and machine learning.
- Apply different data modeling techniques such as relational modeling (e.g., star schema, snowflake schema), dimensional modeling, and potentially Data Vault modeling based on requirements.
- Ensure data models are scalable, maintainable, and aligned with business needs.
- Azure Data Platform Expertise:
- Demonstrate deep expertise in a wide range of Azure data services, including but not limited to:
- Storage: Azure Data Lake Storage Gen2, Azure Blob Storage.
- Data Processing: Azure Data Factory, Azure Databricks (PySpark, Spark SQL), Azure Stream Analytics, Azure Synapse Pipelines.
- Data Warehousing: Azure Synapse Analytics (Dedicated SQL Pools, Serverless SQL Pools), Azure SQL Database.
- NoSQL Databases: Azure Cosmos DB.
- Data Governance: Azure Purview.
- Orchestration: Azure Logic Apps, Azure Functions.
- Monitoring: Azure Monitor, Azure Log Analytics.
- Architect and implement solutions for data integration across diverse data sources, both on-premises and in the cloud.
- Optimize Azure data services for performance, cost-efficiency, and scalability.
- Implement security best practices for Azure data services, including access control, encryption, and network security.
- Collaboration and Leadership:
- Collaborate effectively with cross-functional teams, including data engineers, data scientists, business analysts, and business stakeholders, to understand data requirements and deliver solutions.
- Provide technical guidance and mentorship to data engineers and other team members.
- Lead the evaluation and adoption of new Azure data technologies and services.
- Communicate complex technical concepts clearly and effectively to both technical and non-technical audiences.
- Participate in the development of data standards and best practices.
- Problem Solving and Innovation:
- Troubleshoot and resolve complex data-related issues.
- Stay up-to-date with the latest trends and advancements in data architecture, data management, and cloud technologies.
- Identify opportunities for innovation and improvement in our data platform and processes.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.
- Minimum of 8 years of experience in data warehousing, data modeling, and data architecture.
- Minimum of 4 years of hands-on experience designing and implementing data solutions on Microsoft Azure.
- Strong understanding of data warehousing concepts, principles, and best practices.
- In-depth knowledge of data lakehouse architectures and their benefits.
- Proven experience in implementing the medallion architecture for data management.
- Excellent data modeling skills, including conceptual, logical, and physical model design.
- Proficiency in SQL and experience with various database systems (relational and NoSQL).
- Hands-on experience with Azure data services such as ADLS Gen2, ADF, Azure Databricks, Azure Synapse Analytics, Azure SQL Database, and Azure Purview.
- Experience with data integration tools and techniques.
- Strong understanding of data governance, data quality, and data security principles.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work independently and collaboratively in a fast-paced environment.
Preferred Qualifications:
- Azure Data Engineer Associate or Azure Solutions Architect Expert certification.
- Experience with big data technologies (e.g., Hadoop, Spark).
- Familiarity with data science and machine learning workflows.
- Experience with DevOps practices and CI/CD pipelines for data solutions.
- Knowledge of Python or other programming languages relevant to data engineering.
- Experience with data visualization tools (e.g., Power BI, Tableau).
