GEN AI Data Architect

GEN AI Data Architect Job Description PRIMARY RESPONSIBILITIES

• Articulate and document a data architecture solution vision
• Develops conceptual, logical, and physical data models, and supports the development of data strategies by building roadmaps and creating processes to meet current and future needs
• Develop data frameworks and scalable approaches to support an evolving data architecture and integration needs that can be leveraged and re-used for new data-driven business products, services, and functions
• Provides recommendations on innovative data platforms, tools and services, and aligns decision-making with the organization''s strategic data vision and all standard policies, processes, and procedures
• Develop and implement cloud data platforms to support product strategy and roadmap, advanced analytics, data science and SQL/NO-SQL datasets to give business, products, and operations a competitive advantage
• Oversees data architecture designs, business requirements, prototypes, testing, and training
• Provide strategic recommendations for technology adoption to improve productivity, collaboration, and efficiency
• Ensure data architecture and practices comply with relevant industry standards and regulations, such as GDPR, HIPAA, and CCPA
• Establish and enforce data quality standards and practices
• Adhere to ethical standards and comply with the laws and regulations applicable to your job function
• Manages a repository of documentation related to data architecture standards, protocols, and frameworks, and regularly evaluates for improvement opportunities
• Regularly review and update architecture documentation to reflect changes in technology and business needs
• Maintain the technology catalog and manage the technology adoption process
• Maintains currency with emerging technologies, leveraging industry knowledge and expertise to drive continuous improvement and innovation efforts in data architecture and related areas (e.g., APIs, LLMs, GenAI, Microservices, Event-based architectures)
• Continuously improve processes, technologies, and platforms to provide best value to business
• Identify cost-savings opportunities through the optimization of existing tools
• Facilitate cross team and cross component collaboration

Technical Expertise:

• Strong expertise in Azure Analysis Services (AAS), Power BI Premium Gen2, Azure Data Factory, Azure Synapse, Data/Delta Lake, Microsoft Fabric, and Data Pipelines.
• Proven experience in designing and implementing data solution on the Azure Platform
• Experience with cloud data platforms (e.g., AWS, Azure, Google Cloud) and data warehousing solutions (e.g., Redshift, BigQuery, Snowflake).
• Strong knowledge of SQL and experience with relational databases (e.g., Oracle, PostgreSQL, SQL Server).
• Experience with Pyspark framework building data lake (S3/Iceburg), data warehouse (on Redshift Serverless/Spectrum), EMR (serverless), pipelines catalogs construction (Glue, Athena)
• Experience with NoSQL databases (e.g., DynamoDB, Cassandra) and big data technologies (e.g., Hadoop, Spark).
• Understanding of ETL processes and tools (e.g., Python). o Knowledge of data governance, data quality, and data security practices

Mandatory Competencies

• Data Science and Machine Learning - Data Science and Machine Learning - Gen AI
• BI and Reporting Tools - BI and Reporting Tools - Power BI
• Cloud - Azure - Azure Data Factory (ADF), Azure Databricks, Azure Data Lake Storage, Event Hubs, HDInsight
• Cloud - AWS - Tensorflow on AWS, AWS Glue, AWS EMR, Amazon Data Pipeline, AWS Redshift
• Cloud - Cloud - Snowflake
• Big Data - Big Data - Pyspark
• Data Science and Machine Learning - Data Science and Machine Learning - Apache Spark
• Data Science and Machine Learning - Data Science and Machine Learning - Databricks
• Data Science and Machine Learning - Data Science and Machine Learning - Python
• Database - Database Programming - SQL
• Cloud - AWS - AWS SNS, AWS SQS, AWS Kinesis
• Beh - Communication and collaboration