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About the role
Description : Role Type : Full-time About UsefulBI : UsefulBI is a leading AI-driven data solutions provider specializing in data engineering, cloud transformations, and AI-powered analytics for Fortune 500 companies. We help businesses turn complex data into actionable insights through our innovative products and services. Overview : UsefulBI is looking for highly skilled candidates, with expertise in generating power business insights from very large datasets, in this space where the primary aim would be to enable needle moving business impacts through cutting edge statistical analysis. We are looking for passionate data engineers who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. The candidate should be well-versed in statistics, R and SAS languages, machine learning techniques, mathematics, and SQL databases. Experience Required : - Minimum 5-10 years Experience in Data Engineering. - Must have good knowledge and experience in Python. - Must have good Knowledge of Pyspark. - Must have good Knowledge of Databricks. - Must have good experience in AWS/Azure. - Typically requires relevant analysis work and domain-area work experience. - Expert in the management, manipulation, and analysis of very large datasets. Key Responsibilities : - Create and maintain optimal data pipeline architecture. - Assemble large, complex data sets that meet functional / non-functional business requirements. - Identify, design, and implement internal process improvements : automating manual processes, etc. - Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS big data technologies. - Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics. - Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs. Description : Role Type : Full-time About UsefulBI : UsefulBI is a leading AI-driven data solutions provider specializing in data engineering, cloud transformations, and AI-powered analytics for Fortune 500 companies. We help businesses turn complex data into actionable insights through our innovative products and services. Overview : UsefulBI is looking for highly skilled candidates, with expertise in generating power business insights from very large datasets, in this space where the primary aim would be to enable needle moving business impacts through cutting edge statistical analysis. We are looking for passionate data engineers who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. The candidate should be well-versed in statistics, R and SAS languages, machine learning techniques, mathematics, and SQL databases. Experience Required : - Minimum 5-10 years Experience in Data Engineering. - Must have good knowledge and experience in Python. - Must have good Knowledge of Pyspark. - Must have good Knowledge of Databricks. - Must have good experience in AWS/Azure. - Typically requires relevant analysis work and domain-area work experience. - Expert in the management, manipulation, and analysis of very large datasets. Key Responsibilities : - Create and maintain optimal data pipeline architecture. - Assemble large, complex data sets that meet functional / non-functional business requirements. - Identify, design, and implement internal process improvements : automating manual processes, etc. - Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS big data technologies. - Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics. - Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
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