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For Companies At a Glance
- Tasks: Design and build data pipelines, ensuring data quality and supporting analytics.
- Company: Join the University of Pittsburgh Athletics' innovative data team.
- Benefits: Flexible remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement.
- Why this job: Make a real impact in sports analytics and data management.
- Qualifications: Bachelor's degree and 5 years of relevant experience required.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Evaluates business requirements, creates advanced data ingestion processes and modeling, and provides extensive support for databases and relevant services. Designs new data architectures. Ensures data quality and delivery. Trains and assists lower-level data engineers; often serves as team lead. Supports data analysts and scientists with expert-level research and consulting.
Who We Are
Panthers Data and Analytics represents the University of Pittsburgh Athletics Department as a part of the University’s Pitt IT Analytics team. Our mission is to “Improve decision-making by managing data from source to strategy.” We help the Athletics Department improve across four domain areas: administration, revenue generation, sports science, and sports analytics.
Who we are looking for
The Data Engineer, Athletics will be the primary individual contributor to data engineering efforts within Panthers Data and Analytics. You bring expertise, energy, and enthusiasm to help our program in the critical work of data infrastructure management, data pipeline development, data modeling, and more using an AWS-centered cloud environment.
Essential Functions
- Develop data pipelines: Design, build, and maintain robust and efficient data pipelines and APIs that collect, process, and integrate data from various sources.
- Curate data for data science and analytics: Curate, organize, and optimize data in data warehouses and lakes to ensure it is accurate, accessible, and ready for various data science and analytics use cases.
- Enhance and expand the data platform: Implement scalable solutions that improve and extend the utility of our data infrastructure and platform(s).
- Facilitate AI/ML operations: Partner with Data Scientists to operationalize machine learning and artificial intelligence models.
- Document engineering work: Document and share details on engineering standards, practices, and workflows.
- Special projects and other duties as assigned.
Requirements
- Bachelor\’s Degree
- Minimum 5 years of experience
- Combination of education and relevant experience will be considered in lieu of education and/or experience requirement.
Work Schedule
M-F bus hrs EST. On occasion, some evening and weekend work may be necessary depending on business load, project timeline requirements, urgent support, special events or scheduled downtime changes. May be responsible for manning an escalation/on-call phone number.
Work Arrangement
Remote: Teams working from different locations (off-campus).
The University of Pittsburgh is an Equal Opportunity Employer.
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Data Engineer, Athletics employer: University of Pittsburgh
Contact Detail:
University of Pittsburgh Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer, Athletics
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those connected to the University of Pittsburgh Athletics. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and projects. When you apply through our website, include links to your work so we can see your expertise in action.
✨Tip Number 3
Prepare for the interview by brushing up on AWS and data architecture concepts. We love candidates who can talk about their experience with data ingestion processes and how they ensure data quality.
✨Tip Number 4
Be ready to discuss collaboration! As a Data Engineer, you'll be working with data analysts and scientists. Share examples of how you've successfully partnered with others to enhance data solutions.
We think you need these skills to ace Data Engineer, Athletics
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with data pipelines, AWS, and any relevant projects. We want to see how your skills align with our mission at Panthers Data and Analytics!
Showcase Your Expertise: Don’t hold back on sharing your technical skills! Whether it’s data modelling or curating data for analytics, let us know how you’ve tackled similar challenges in the past. We love seeing real-world examples of your work.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless it’s necessary. We appreciate a well-structured application that gets straight to the point!
Apply Through Our Website: We encourage you to submit your application through our official website. It’s the best way to ensure we receive all your details and can review your application thoroughly. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at University of Pittsburgh
✨Know Your Data Pipelines
Make sure you can discuss your experience with designing and maintaining data pipelines. Be ready to share specific examples of how you've built robust systems in the past, especially in an AWS environment.
✨Showcase Your Collaboration Skills
Since you'll be working closely with data scientists and analysts, highlight any past experiences where you've successfully collaborated on projects. Discuss how you facilitated AI/ML operations or supported team members in their data needs.
✨Prepare for Technical Questions
Brush up on your technical knowledge related to data ingestion processes, data modelling, and database management. Expect questions that test your understanding of these concepts and be prepared to solve problems on the spot.
✨Demonstrate Your Passion for Data
Let your enthusiasm for data engineering shine through. Share why you love working with data, how you stay updated with industry trends, and any personal projects that showcase your skills and commitment to the field.