About NCSA College Recruiting

NCSA College Recruiting is the world largest college recruiting platform, providing student-athletes content, tools, coaching and access to a network of 40,000 college coaches across 37 sports. NCSA is an online experience of IMG Academy, the world’s leading sports education brand and one of the Best and Brightest Companies to Work For in the Nation in 2024 and Top Remote Places to Work in 2024. IMG Academy provides a holistic education model that empowers student-athletes to win their future, preparing them for college and for life. Additional on-campus and online experiences include:

• Boarding school and camps, via a state-of-the-art campus in Bradenton, Fla.
• Online coaching via IMG Academy+, with a focus on personal development through the lens of sport and performance

Position Summary:

The Senior Data Engineer is responsible for implementing the technical direction of the Data Engineering team. This is a hands-on, individual contributor role operating on a cross-functional and skill-focused team. The Data Engineering team enables data- and insight-driven decision-making and planning by providing consistent, accurate and timely data to analysts, data scientists and business users. Working closely with platform development managers, Data Engineers design, implement and maintain data infrastructure used by Product and Platform engineering teams in the form of data pipelines, data lakes and warehouses, business intelligence and visualization tools, and services for data quality and governance. Working with analysts and data scientists, they build custom data integrations, develop and deploy machine learning endpoints, and optimize the performance and scalability of customer-facing and internal data applications. The Data Engineer’s work provides teams with visibility into NCSA’s business performance, product delivery, and the outcomes student athletes, coaches and teams achieve to realize their goals. Reporting to the Director, Data Engineering, the role requires deep expertise in the design, implementation, and continuous improvement of cloud-native data infrastructure and platforms.  

 
Position Responsibilities:  

• Design, develop and maintain automated data pipelines to standardize and refine data collection for existing and new data sources used to power analytics and data science for business teams. 

• Develop extensive subject-matter expertise around individual and categories of data pipelines to establish operating standards and service-level agreements with analysts and data scientists. 

• Forecast data utilization and identify potential bottlenecks or optimization opportunities.  

• Apply test-driven development methods to data platform configuration and deployment. 

• Work with Software Engineering teams to adopt standard data platform components for their systems with the confidence that event data will be safely collected, stored, and transformed. 

• Lead technical conversations and decision-making around data architecture and implementation with Engineering teams, helping to identify opportunities and recommend technical solutions. 

• Partner with Analysts, Data Scientists and Data Program Managers to apply, implement and production-alize descriptive, predictive, and prescriptive statistical models on large datasets. 

• Define data operations approach and operating methodology for versioning, testing, security, test data management, data migrations, data quality, metadata, and documentation.  

• Establish standards for building reliable, highly available, testable, and sustainable data platforms using performance engineering, continuous data delivery, and instrumentation for observability. 

• Dedicate meaningful time to research, evaluation and implementation of new tools and methods (Open Source and commercial) to meet changing organizational data needs and opportunities. 

• Serve as part of an on-call support rotation with other engineers to debug, troubleshoot and resolve data platform issues using the Incident Command System methodology. 

 Knowledge, Skills and Abilities:

• You have expertise (5+ years) in data engineering, data modeling (physical and logical), dimensional modeling, storage, resilience, and security. 

• You have sufficient experience in the mechanics of software engineering (preferably Python) practices to teach. 

• You have sufficient experience in the mechanics of scalable data infrastructure to teach. 

• You have deep expertise with cloud-based distributed data platforms, preferably in AWS.  

• You have experience developing data pipelines using Airflow, performing ELT design, and utilizing data storage systems such as S3, Redshift or similar tools. 

• You have implemented batch and streaming data pipelines at scale and built platform components with automated testing suites using test-driven development principles for data.   

• You are familiar with relational databases (e.g., Postgres). 

• You have deep knowledge of SQL, Python, and dbt. 

• You know how to create efficiency in data handling by tracking the lineage, maintaining the quality, and improving the discoverability of data. 

• You have learned painful lessons from building successive generations of software to manage and deploy highly available, reliant, and performant data infrastructure at scale in high availability, high volume, performant and distributed environments. 

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