Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent in various US locations as we continue to build the best analytics global consulting team in the world.
-Define process to analyze and map specific data hierarchies across functions, customers, and systems.
-Collaborate with Data Management Leads to ensure the model and lexicon of the data assets meets users' needs.
-Define sources and calculations for metrics and KPIs. Lead metrics standardization in functions, reconciling data as necessary.
-Align hierarchy with corresponding business rules, requirements, and data transformations.
-Define and manage an exception process to manage hierarchies and cross functional metrics.
-Develop and maintain data models.
-Build technical specs and map documentation.
-Partner with IT to develop data architecture standards and guidelines for processing and persisting data on prim and cloud.
-Partner with IT to ensure appropriate standardization, categorization, and hierarchy is followed.
-Undergraduate Degree in Computer Science, Engineering, Mathematics, Statistics, or similar field.
-Minimum 5 years of experience in data architecture.
-Strong SQL abilities.
-Deep understanding of data structure and cross-functional hierarchies to address business needs.
-Ability to translate business requirements into technical requirements and reverse engineering.
-Ability to communicate data hierarchy management procedures to non-technical stakeholders.
-Ability to gather requirements to create conceptual, logical, and physical models.
-Ability to perform data profiling to create business mapping and technical design.
-Knowledge of Cloud tools and systems; Azure preferred.
-Knowledge of data storage systems, data factory, and data lake.
-Knowledge of cleansing data and building data pipelines.
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