Senior Data Engineer

Fanatics Commerce•Tampa, FL

2d•Hybrid

About The Position

Fanatics Commerce is the global leader in licensed sports merchandise, operating a vertically integrated platform that designs, manufactures, and delivers officially licensed apparel, jerseys, headwear, and collectibles for major leagues, teams, and events worldwide. With more than 900 e-commerce sites and a global omnichannel presence across digital, in-venue, and retail, Fanatics Commerce reaches fans in over 180 countries and powers official fan experiences for many of the world's iconic sports properties. At Fanatics, we bring our BOLD Leadership Principles to life every day - building championship teams, obsessing over fans, acting with entrepreneurial speed, and delivering with a determined and relentless mindset. Software Development & API Services (10%): Design, develop, and maintain software applications, backend services, and RESTful APIs to ensure performance, scalability, and reliability using Python, Django, Flask, SQL-based databases, and version control tools; perform testing, troubleshooting, documentation, and ongoing application support. Analytics & Visualization Solutions (25%): Design, deploy, and maintain analytical and visualization solutions using BI and analytics tools to monitor core metrics, generate operational alerts, and enable self-service analysis; define data requirements, optimize analytical workflows, and translate complex data into clear, actionable insights that support business decision-making. Analytics Platform Enablement & Data Quality Management (15%): Support effective use of BI and analytics platforms through training, guidance, and best practices; implement and oversee data quality, governance, and management processes to ensure consistent, accurate, and reliable enterprise reporting. Data Pipeline Engineering & Data Integration (30%): Design, build, and maintain scalable data pipelines and ETL/ELT workflows using cloud-based data platforms and orchestration tools; integrate structured and unstructured data from internal and external sources; automate workflows, monitoring, recovery, and CI/CD processes; evaluate and implement enhancements to data engineering tools and frameworks. Operational Data Engineering & Analytical Support (20%): Provide production support for data systems, including incident response, on-call support, troubleshooting, performance tuning, and root-cause analysis; conduct ad hoc data investigations to validate data quality, address anomalies, and support business and operational inquiries.

Requirements

Bachelor’s Degree or U.S. equivalent in Computer Science, Information Technology, Information Systems, Computer Engineering, or a related field.
5 years of professional experience as a Data Engineer, Software Developer, or any occupation, job title or any position involving the development of pipelines, or software applications.
5 years of professional experience designing data ingestion, modeling new data sources, and creating Source-to-Target documentation.
5 years of professional experience architecting data pipeline orchestration frameworks or software applications, including scheduling, error handling, notification, and restartability processes.
5 years of professional experience developing, and maintaining software applications and backend services, including implementing RESTful APIs and microservices to support data processing, system integration, and reporting.
3 years of professional experience gathering requirements, collaborating with users and cross-functional teams including product managers, QA engineers, refining BI or software development specifications, and delivering scalable technical solutions.
3 years of professional experience performing data profiling, pipeline monitoring, defect resolution, performance tuning to ensure data quality and operational efficiency.
3 years of professional experience conducting designing, unit testing, integration testing, data validation, and system-level troubleshooting to ensure high-quality and reliable software or data pipelines.
2 years of professional experience delivering extensive self-service BI/software training, and preparing technical documentation, including design specifications, API documentation, and usage guides to support user adoption and system governance.
2 years of professional experience architecting data infrastructure components, designing and developing analytical tools including dashboards and reports to monitor KPIs, provide alerts, and support exploratory data analysis and integrate with backend application logic.
1 years of professional experience utilizing data visualization and UI/UX principles to present information for optimal consumption, facilitating quick understanding and decision-making.
1 years of professional experience promoting adoption of BI and software features by applying iterative design refinement, enhancing user experience, and conducting user training sessions.

Responsibilities

Design, develop, and maintain software applications, backend services, and RESTful APIs using Python, Django, Flask, SQL-based databases, and version control tools.
Perform testing, troubleshooting, documentation, and ongoing application support.
Design, deploy, and maintain analytical and visualization solutions using BI and analytics tools.
Monitor core metrics, generate operational alerts, and enable self-service analysis.
Define data requirements, optimize analytical workflows, and translate complex data into clear, actionable insights.
Support effective use of BI and analytics platforms through training, guidance, and best practices.
Implement and oversee data quality, governance, and management processes.
Design, build, and maintain scalable data pipelines and ETL/ELT workflows using cloud-based data platforms and orchestration tools.
Integrate structured and unstructured data from internal and external sources.
Automate workflows, monitoring, recovery, and CI/CD processes.
Evaluate and implement enhancements to data engineering tools and frameworks.
Provide production support for data systems, including incident response, on-call support, troubleshooting, performance tuning, and root-cause analysis.
Conduct ad hoc data investigations to validate data quality, address anomalies, and support business and operational inquiries.