Hybrid Data Engineer: Build Scalable Pipelines & AI Tools

Fanatics; Sunnyvale (TX); Hybrid; USD 162,000 - 182,000; Full time

Job summary

Fanatics is seeking a data scientist to design, build, and maintain scalable data pipelines and a centralized data platform that ingests data from diverse sources for analytics.

You will implement ETL/ELT processes, enable self-service data access with data services and AI-powered tools, and collaborate with cross-functional teams to deliver reliable insights for business decisions while following CI/CD and cloud best practices.

Qualifications

• 4 years of professional experience collaborating with cross functional stakeholders to translate business requirements into scalable analytical solutions
• 4 years of professional experience applying software engineering best practices to analytical codebases, including version control (Git), peer code reviews, and modular code development
• 3 years of professional experience writing complex SQL queries and performing data mining across large relational or cloud-based data warehouse environments
• 3 years of professional experience designing and developing data pipelines using Python (including Pandas and PySpark) and ETL processes to transform and prepare large datasets for analytical use
• 3 years of professional experience developing and optimizing data models, custom datasets, and data structures to support evolving analytical and reporting needs
• 3 years of professional experience designing and developing business intelligence dashboards and automated reporting solutions SQL and visualization tools (including Tableau, QuickSight, or Looker) to support KPI tracking and executive reporting
• 3 years of professional experience maintaining and optimizing cloud-based data solutions within AWS environments to ensure performance, reliability, and scalability
• 3 years of professional experience conducting statistical analyses, including hypothesis testing, regression analysis, and product experimentation to inform business decision-making

Responsibilities

• Design, build, and maintain scalable data pipelines via Airflow and Dagster
• Develop a centralized data platform integrating diverse sources for analytics
• Implement ETL/ELT to structure raw data into curated datasets
• Enable self-service data access with data services, semantic layers, and AI-powered tools
• Collaborate with data scientists, analysts, and stakeholders to define data requirements
• Design and maintain monitoring, logging, and alerting for pipeline reliability
• Apply CI/CD and other software engineering practices to data infrastructure in the cloud

Skills

Cross-functional collaboration

Software engineering best practices

SQL

Python (Pandas, PySpark)

ETL/ELT design

Data modeling

BI dashboards and automated reporting

AWS cloud data solutions

Statistics for experimentation

Education

Bachelor’s Degree in CS/CE/Eng Mgmt or related field

Tools

Git

Tableau

Looker

QuickSight

Power BI

SQL-based data warehouses

PySpark

Pandas

Job description

Fanatics is seeking a data scientist to design, build, and maintain scalable data pipelines and a centralized data platform that ingests data from diverse sources for analytics.

You will implement ETL/ELT processes, enable self-service data access with data services and AI-powered tools, and collaborate with cross-functional teams to deliver reliable insights for business decisions while following CI/CD and cloud best practices.