Job description

Required Qualifications Background in Microsoft Azure, Azure Data Lake, MDM and Enterprise Data Platforms. 15+ years of Exp. 5+ years of experience with deep understanding in both traditional and modern data architecture and data processing, performance, and scalability concepts 5+ years of experience with traditional relational SQL Databases (mySQL, Postgres, SQLServer or Oracle SQL) and noSQL (Mongo, DynamoDB, Cosmos, etc.) 3+ years of experience with streaming analytics using Kafka 2+ years of experience with Integration and processing from multiple data sources using tools such as Azure Data Factory (MUST HAVE), AWS Glue or similar tools such as Talend or Informatica (GOOD TO HAVE) 2+ Years of experience around Master Data Management (MDM) strategies. 5+ years of architecture, design, implementation, and/or support of complex application architectures Deep understanding of data and how it relates to architecture (Data ingestion, Realtime, Batch, SQL, NoSQL, Analytics, etc.) Understanding the concept of data cleansing techniques including, but not limited to, outlier removal, data imputation, data standardization and aggregation. Create a data factory, orchestrate data processing activities in a data-driven workflow, monitor and manage the data factory, move, transform and analyze data. Understanding of modern SDLC processes (Agile, CI, CD, etc.) and how architecture fits Strong ability to think strategically and align design and architecture patterns to drive current needs as well as future growth Strong communication skills with the ability to tailor technical communications to any level (Exec to Jr. Software Engineers) Ability to break a problem of any size into manageable pieces Deep understanding of enterprise integration with an emphasis on scalable patterns Ability to draft proposals of cost vs. use/benefit of proposed architectural changes Work closely with delivery teams including business subject matter experts (SMEs) to deliver data warehousing solutions that are reliable, flexible, scalable, and provide low cost ownership. • Provide technical mentoring, guidance, and training to Data and Analytic engineering resources

Requirements