Kalshi

Sports Operations Engineer

New York, New York, United States

Sponsorship not specified$100k-$150kDetected 265 days ago

JavaScriptPythonSQLNoSQLWebSocketsAirflowData EngineeringData ScienceAI OrchestrationStatisticsDesign SystemsProduct ManagementExcelCommunication

About the role

Sports Operations Engineer - Kalshi Kalshi is defining a new category Kalshi has defined a new category: prediction markets.
Kalshi allows people to trade on the outcome of any events and turn any question about the future into a financial asset.
Kalshi fought for years and legalized prediction markets in the US for the first time in history.

Design and build Python automation scripts that list, update, and determine sports markets at scale.
Create intelligent systems that handle pre-game, in-play, and futures markets across NFL, NBA, MLB, NHL, MLS, NCAA, and international competitions without manual intervention.
Build robust data pipelines that ingest, process, and structure large volumes of sports data from multiple feeds.
Design systems that handle real-time game data, player statistics, injury reports, weather updates, and official league sources while maintaining data integrity and low latency.
Implement and maintain complex DAGs for orchestrating market operations workflows.
Design fault-tolerant systems that handle dependencies between data ingestion, market creation, live updates, and settlement processes across thousands of concurrent sporting events.
Develop and maintain cron jobs and scheduled tasks that automatically list markets at optimal times, update odds based on market conditions, and settle markets immediately after game completion.
Build systems that understand overtime rules, tiebreakers, playoff scenarios, and sport-specific nuances.
Implement logic for handling postponements, suspensions, protests, and stat corrections across different leagues.
Create systems that process live game feeds and trigger appropriate market actions in real-time.

To excel in this role, you should possess the following qualifications and skills:
Experience pulling, processing, and structuring large datasets from APIs, webhooks, and real-time data feeds. Familiarity with data formats common in sports data (JSON, XML, WebSocket streams).
Proven ability to build automation and data pipelines that handle high-volume, time-sensitive operations with strong error handling and recovery mechanisms.
Track record of making continuous tooling improvements and building internal products that scale with team needs.

1-3 years of experience as a software engineer with a proven track record of building production systems.
Strong proficiency in Python and JavaScript with experience building automation scripts, data processing systems, and web applications.
Experience pulling, processing, and structuring large datasets from APIs, webhooks, and real-time data feeds.
Familiarity with data formats common in sports data (JSON, XML, WebSocket streams).
Product ownership mentality with experience gathering requirements, prioritizing features, and delivering tools that solve real operational problems.
Strong knowledge of sports and sports trading operations including understanding of different market types, settlement rules, and common edge cases across major sports leagues.

Bachelor's degree in Computer Science, Data Science, or related technical field.
Experience with DAG frameworks (Airflow, Prefect, Dagster) for orchestrating complex workflows and data pipelines.
Expertise with cron jobs and scheduled task management in production environments, including monitoring and error handling.
Experience with sports data providers is strongly preferred.
Database expertise with both SQL and NoSQL systems for storing and querying sports data efficiently.
Experience with real-time systems including WebSockets, event streaming, and message queues.
Strong debugging and troubleshooting skills with ability to quickly diagnose and fix issues in production systems.
NYC Pay Transparency Disclosure
Gather requirements from operators and continuously improve tooling to increase efficiency and reduce errors.
Continuously improve system performance to handle increasing market volumes.

$100,000 to $150,000 annually plus equity and benefits.
This salary range is based on the current available market data, and represents the expected salary range for this role.
Should you have compensation expectations that exceed these bands, we'd love to hear from you and would welcome you to reach out to further discuss.

We are committed to our vision of building something big… but also useful: a product that brings more truth through the power of markets.

bring more truth to the world through the power of markets.
Building a new category is hard… like really hard.
But it's beautiful and deeply fulfilling.
we hire really talented people, work really hard, and enjoy the climb.
We are looking for ambitious and exceptional people to join our (relatively small) team to help us build the next generation of financial markets.
As a Sports Operations Engineer at Kalshi, you'll be the technical force behind our sports markets infrastructure.
You'll build and maintain the automation systems that power thousands of sports markets across all major leagues.
This role combines deep sports knowledge with strong engineering skills to create scalable solutions for market listing, monitoring, and settlement.
We're scaling rapidly, which means your role will evolve and grow with the company; your initial responsibilities will include:
Our culture is simple: we hire really talented people, work really hard, and enjoy the climb.
Role Roadmap
Automation Development: Design and build Python automation scripts that list, update, and determine sports markets at scale.
Data Pipeline Architecture: Build robust data pipelines that ingest, process, and structure large volumes of sports data from multiple feeds.

Commitment to Equal Opportunity
Kalshi is committed to creating a culture of inclusion and belonging, and we are proud to be an equal opportunity employer.

This listing is sourced directly from Kalshi's careers page and normalized into a canonical job model.