Staff Analytics Engineer

FanDuel•New York City, NY

About The Position

FanDuel is hiring a Staff Analytics Engineer for its Sports Modeling & Innovation team. This senior technical leadership role will help expand and improve auxiliary data that can be joined to customer bet data. The engineer will set technical direction for sourcing, ingesting, modeling, and serving data assets, and design, build, and scale the models, pipelines, and architecture used by data scientists, analysts, and traders to support business decisions. The first year will involve hands-on work, including real-time data ingestion, building scrapers, and establishing a medallion architecture. The initiative will begin with NBA and NFL data before expanding to international soccer, which will require substantially more work. The role also involves mentoring analytics engineers and improving the team's engineering practices, standards, and ways of working. The successful candidate will be curious, action-oriented, and comfortable making decisions in an ambiguous, fast-moving environment.

Requirements

7+ years of experience in an analytics or data engineering role, including senior- or lead-level responsibilities.
4+ years designing and maintaining databases and data ingestion pipelines; real-time ingestion and web scraping experience are ideally included.
Deep understanding of relational databases.
Proficiency in SQL, Databricks, R or Python, Tableau, AWS S3 buckets, and command line.
Proven experience building production data pipelines with Python or R and Databricks, and hands-on experience designing medallion (bronze/silver/gold) or comparable layered architectures.
Bachelor's degree, preferably in a highly numerate major such as Math, Statistics, or Economics, or relevant experience in a data analysis role.
Strong organizational and time-management skills.
Ability to work in a fast-paced environment.
Strong problem-solving skills.
Demonstrated experience mentoring engineers and improving a team's technical practices, standards, and ways of working.
Comfort operating amid ambiguity in a scrappy, early-stage build and making sound architecture and prioritization decisions with incomplete information.

Nice To Haves

Curiosity about AI and emerging technologies.
Exposure to sports data, especially NBA, NFL, and international soccer.

Responsibilities

Architect and build pipelines for fast, often real-time, ingestion of auxiliary data into well-designed stores, including SQL and Delta Lake, and establish new data sources from scratch.
Build and maintain scrapers and validation pipelines that source external data, flag inaccurate or missing data, and adapt as new datasets and sports are introduced.
Own the design and delivery of the global data project's medallion architecture and transformation jobs; set codebase standards and troubleshoot complex issues across the stack.
Establish scalable database management and automated data-cleaning approaches for large, messy, heterogeneous datasets.
Make memory-versus-speed tradeoffs to meet project needs, improve query execution times, and remove unnecessary or unused metrics.
Partner with stakeholders to prioritize and deliver data products, including curated datasets, Tableau dashboards, and R Shiny apps.
Mentor analytics engineers and promote best practices, coding standards, and design patterns across the team.
Influence technical strategy and the project roadmap, making pragmatic build-versus-buy and architecture decisions in an ambiguous, fast-moving environment.
Perform other duties as assigned by the Company.