Senior Data Engineer

We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on our know-how and technology to boost their business. We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on our know-how and technology to boost their business. What We Offer A collaborative environment with colleagues from all over the world (Offices in Europe, Asia and US) including various social events and teambuilding. Flexibility to manage your workday and tasks with autonomy. A balance of structure and autonomy to tackle your daily tasks. Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants. Global Employee Assistance Programme. Calm and Reulay app (leading well-being apps designed to support focus, quality rest, mindfulness, and long-term mental resilience). Online training videos. Flexible working hours. A collaborative environment with colleagues from all over the world (Offices in Europe, Asia and US) including various social events and teambuilding. Flexibility to manage your workday and tasks with autonomy. A balance of structure and autonomy to tackle your daily tasks. Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants. Global Employee Assistance Programme. Calm and Reulay app (leading well-being apps designed to support focus, quality rest, mindfulness, and long-term mental resilience). Online training videos. Flexible working hours. Requirements Master’s degree in Computer Science, Computer Engineering, or closely related field and 1 year experience as a data engineer or related occupation. Must Possess 1 Year Of Experience With Python, Java, Kafka, AWS, and Docker ETL Development and Warehousing Analytic and debugging using SQL Agile development environment Designing data architecture Master’s degree in Computer Science, Computer Engineering, or closely related field and 1 year experience as a data engineer or related occupation. Must Possess 1 Year Of Experience With Python, Java, Kafka, AWS, and Docker ETL Development and Warehousing Analytic and debugging using SQL Agile development environment Designing data architecture Responsibilities Design, build, and implement generalized large-scale, sophisticated data pipelines using Nifi for downstream analytics and data science for our Sport Performance products. Design and develop scalable Nifi ingestion pipelines within AWS cloud services to consume real-time and batch data from external sources. Ensure the seamless integration of AWS-based tools for data storage, processing, and analytics. Responsible for ETL development and warehousing using Python and Java. Create data pipeline triggers and filters within ETL (extract, transform, and load) process to ensure appropriate optimization of data flowing through system and resource usage. Implement monitoring and error handling for all new parts of the data pipeline to ensure observability and alerting is available. Establish rigorous unit testing across the data pipeline to ensure robustness of the system. Design and create data models for use throughout the ETL system. Utilize Kafka to efficiently and to effectively store data to move throughout the data pipeline and for downstream data science and analytics usage. Design data architecture and data models for both internal and external representations of data. Build the data transforms within the data pipeline to convert data from external to internal representations. Conduct data analytics and debugging of bad data by writing SQL queries. Build automated cleaning of data to remove bad or unusable data from downstream consumers with logging to understand the frequency and depth of the underlying issues. Collaborate with other engineering teams to adopt standard methodologies, drive scalability, and increase consistency across systems. Maintain awareness of company standards and technology guidance; use JIRA, an Agile project mgmt. tool, to ensure efficient data development; collaborate with peers to align projects with overall direction. Follow best practices across Data Engineering to ensure scalable, consistent data architecture and system. Utilize Java language to build data processor in Nifi framework. Utilize Docker to ensure consistent, repeatable, and isolated environment for software development and testing. Work in a self-driven, independent fashion to meet Sport driven deadlines. Position permits telecommuting from anywhere in the U.S. Design, build, and implement generalized large-scale, sophisticated data pipelines using Nifi for downstream analytics and data science for our Sport Performance products. Design and develop scalable Nifi ingestion pipelines within AWS cloud services to consume real-time and batch data from external sources. Ensure the seamless integration of AWS-based tools for data storage, processing, and analytics. Responsible for ETL development and warehousing using Python and Java. Create data pipeline triggers and filters within ETL (extract, transform, and load) process to ensure appropriate optimization of data flowing through system and resource usage. Implement monitoring and error handling for all new parts of the data pipeline to ensure observability and alerting is available. Establish rigorous unit testing across the data pipeline to ensure robustness of the system. Design and create data models for use throughout the ETL system. Utilize Kafka to efficiently and to effectively store data to move throughout the data pipeline and for downstream data science and analytics usage. Design data architecture and data models for both internal and external representations of data. Build the data transforms within the data pipeline to convert data from external to internal representations. Conduct data analytics and debugging of bad data by writing SQL queries. Build automated cleaning of data to remove bad or unusable data from downstream consumers with logging to understand the frequency and depth of the underlying issues. Collaborate with other engineering teams to adopt standard methodologies, drive scalability, and increase consistency across systems. Maintain awareness of company standards and technology guidance; use JIRA, an Agile project mgmt. tool, to ensure efficient data development; collaborate with peers to align projects with overall direction. Follow best practices across Data Engineering to ensure scalable, consistent data architecture and system. Utilize Java language to build data processor in Nifi framework. Utilize Docker to ensure consistent, repeatable, and isolated environment for software development and testing. Work in a self-driven, independent fashion to meet Sport driven deadlines. Position permits telecommuting from anywhere in the U.S. #J-18808-Ljbffr