Data Engineer-Data Management and Analytics-Data Ops/Data Analyst

Responsibilities

 

· 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.

Qualifications

· 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.