Data Scientist with Data Modeler

Job Summary: Work with business users to obtain data requirements and analyze source systems data. Design and develop conceptual, logical and physical data models for the operational and analytical data models. Work closely with the technical and application architects, and developers to implement the data architecture and application solution. Communicate physical database designs to, and work with, database administrator to implement databases. Document data flows, identify appropriate data sources, entities, relationships and redundancies. Manage data integrity and data quality. Maintain technical & business metadata. Generate metadata for Hadoop ingestion for different ingestion frameworks. Design data structures and document data meanings, definitions and intended use, working with various departments/leaders and data stewards to make the metadata available. Define and document the data architecture for operational databases, reporting and provisioning platforms, including the physical components and their functionality. Support data strategies, standards, guidelines, data quality process and governance across the enterprise. Provide leadership in researching and architecting emerging technology solutions to meet business strategies and objectives. Required Skills: Relational and dimensional data modeling and database design, Erwin (Data modeling) skills. Strong SQL, Python, and some Spark knowledge. Hands-on Hive (Hadoop) experience. Knowledge of data architectural approaches, patterns and emerging technologies (RDBMS, Hadoop, and Cloud). Exposure and knowledge of Cloud platforms, cloud architecture, capabilities and processes like GCP, Azure is a great asset. Ability to translate business needs into technical solutions. Strong analytical skills. Experience creating architecture and strategy artifacts. Experience with XML, JSON, Copybook, CSV files ingestion. Experience working in an Agile environment. Knowledge of Data Governance processes for metadata and lineage management is a strong asset. Exposure to any Data Governance tools like IGC, Collibra is an asset. Domain knowledge in financial services and enterprise functions is a strong asset. Knowledge of database administration, database performance tuning, source & target data structures, ETL processes & products. Competencies:

Data Concepts & Data Modelling

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