Staff Analytics Engineer

FanDuel•New York, NY

1d•$159,000 - $199,000

About The Position

FanDuel is hiring a Staff Analytics Engineer for its Sports Modeling & Innovation team. This senior technical leader will help expand and improve auxiliary data that can be joined to customer bet data, setting the technical direction for sourcing, ingesting, modeling, and serving new data assets. The role designs, builds, and scales data models, pipelines, and architecture that support business decisions by data scientists, analysts, and traders. The work begins with a hands-on build, including real-time data ingestion, scrapers, and establishing a medallion architecture from the ground up. Initial sports focus will be NBA and NFL, followed by international soccer, which will require substantially more data work. The engineer will also mentor analytics engineers and improve engineering practices, standards, and ways of working across the team. The role requires curiosity, initiative, and comfort making decisions in an ambiguous, fast-moving environment.

Requirements

At least 7 years of experience in an analytics or data engineering role, including senior- or lead-level responsibilities.
At least 4 years designing and maintaining databases and data ingestion pipelines, ideally including real-time ingestion and web scraping.
Deep understanding of relational databases.
Proficiency in SQL, Databricks, R or Python, Tableau, AWS S3 buckets, and command-line tools.
Demonstrated experience building production data pipelines with Python or R and Databricks.
Hands-on experience designing medallion (bronze/silver/gold) or comparable layered architectures.
Bachelor's degree, preferably in a highly numerate major such as mathematics, 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 working with ambiguity in a scrappy, early-stage build and making architecture and prioritization decisions with incomplete information.

Nice To Haves

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

Responsibilities

Architect and build pipelines for fast, often real-time, ingestion of auxiliary data into well-designed data stores, including SQL, Delta Lake, and medallion architecture.
Build and maintain scrapers and validation pipelines to source external data, flag bad or missing data, and adapt as datasets and sports are added.
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 approaches to database management and automated data cleaning across large, messy, heterogeneous datasets.
Balance memory use and speed to meet project needs, reduce query execution times where possible, 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 the technical strategy and roadmap for the global data project, making pragmatic build-versus-buy and architecture decisions.
Perform other duties as assigned by the Company.