Collaborate with client stakeholders to analyze and synthesize the client data to meet the business objective, reporting dashboard, and descriptive/predictive/prescriptive analytic requirements.
Leverage best practices and industry leading tools and technologies to profile data, develop efficient ingestion, and build semantic data layers.
Design data models and solutions for analytical and reporting use cases.
Build and maintain scalable data transformation workflows in dbt (data build tool) and Snowflake, ensuring clean, consistent, and well-modeled data sets that serve as the foundation for analytics and reporting.
Implement data quality testing, documentation, and version control within dbt to enforce best practices in data governance and maintain transparency across the analytics lifecycle.
Develop efficient, reusable data pipelines and Tableau use case layers that enable cross-functional teams to perform self-service analytics and advanced data exploration with minimal engineering dependency.
Requirements
Master’s or Bachelor's degree in Data Science, Computer Engineering, Math, Statistics, Economics or related analytics field from top-tier universities with strong record of achievement.
3-5 years of experience with solid data engineering skills.
Hands-on knowledge of traditional relational data warehouse technologies, such as SQL (including Analytical SQL functions), dbt (data build tool), Tableau, Python/Pyspark.
Experience in data architecture and data modeling, including creating Semantic Layer Data Models.
Preferred expertise in Sports industry.
Experience in Machine Learning or NLP, such as Scikit-Learn, SpaCity, Pytorch, or Spark NLP is desirable.
Prior experience in management consulting and/or analytics based consulting is desirable.
Job title
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