What you'll do :

• Play a key role in driving a number of data and analytics initiatives including cloud data transformation, data governance, data quality, data standards, CRM, MDM, Generative AI and data science.
• Define cloud reference architectures to promote reusable patterns and promote best practices for data integration and consumption.
• Guide the data science team in implementing data models and analytics models.
• Serve as a data science architect delivering technology and architecture services to the data science community.
• In addition, you will also guide application development teams in the data design of complex solutions, in a large data eco-system, and ensure that teams are in alignment with the data architecture principles, standards, strategies, and target states.
• Create, maintain, and govern architectural views and blueprints depicting the Business and IT landscape in its current, transitional, and future state.
• Define and maintain standards for artifacts containing architectural content within the operating model.

What makes you a great fit :

• Strong cloud data architecture knowledge (preference for Microsoft Azure)
• Experience developing architecture strategies and plans to enable cloud data transformation, MDM, data governance, and data science capabilities.
• Design reusable data architecture and best practices to support batch/streaming ingestion, efficient batch, real-time, and near real-time integration/ETL, integrating quality rules, and structuring data for analytic consumption by end uses.
• Ability to lead software evaluations including RFP development, capabilities assessment, formal scoring models, and delivery of executive presentations supporting a final recommendation.
• Well versed in the Data domains (Data Warehousing, Data Governance, MDM, Data Quality, Data Standards, Data Catalog, Analytics, BI, Operational Data Store, Metadata, Unstructured Data, non-traditional data and multi-media, ETL, ESB).
• Experience with cloud data technologies such as Azure data factory, Azure Data Fabric, Azure storage, Azure data lake storage, Azure data bricks, Azure AD, Azure ML etc.
• Experience with big data technologies such as Cloudera, Spark, Sqoop, Hive, HDFS, Flume, Storm, and Kafka.