Full Stack Software Engineer, Integrity

National Basketball Association (NBA)•New York, NY

10d•$155,000 - $180,000•Hybrid

About The Position

The NBA’s Basketball Strategy & Growth department is seeking a highly skilled full stack software engineer to support, manage, and improve the AI-driven software the Integrity Team uses to identify conduct that may violate the league’s gaming policy. The software ingests data from a wide range of sources — betting markets, basketball data, open-source intelligence (OSINT), and internal league systems — and surfaces anomalies for review by the Integrity Team. The engineer will own the platform end to end: building and maintaining backend jobs and services that power application functionality and designing the frontend interfaces the team uses to triage, investigate, and document the content the system surfaces. This role joins a team of data scientists, engineers, and basketball experts and collaborates closely with the Legal department on any investigation relating to a potential violation of the gaming policy. The work is highly confidential and often time-sensitive, and the engineer will be expected to translate the needs of data scientists, investigators and attorneys into reliable, auditable software.

Requirements

Proficiency in Python and modern JavaScript/TypeScript, with production experience on both the server and client sides of an application.
Experience building and operating backend services and scheduled or orchestrated jobs on cloud infrastructure (AWS, Azure, or GCP).
Understanding of sports betting markets — odds, line movement, limits, player props, and market maker behavior — and of basketball data such as play-by-play, box score, and tracking data.
Strong SQL skills and familiarity with relational data modeling and cloud data warehouses such as Snowflake.
Experience with a modern front-end framework and demonstrated ability to design usable interfaces for analytical or investigative workflows.
Familiarity with Git-based development workflows (GitHub or Azure DevOps), CI/CD, containerization (Docker), and automated testing.
Experience integrating third-party APIs and building robust applications around external services with evolving, incomplete, or inconsistent data.
Strong communication skills and the ability to work effectively with non-technical stakeholders, including attorneys and investigators.
Sound judgment and discretion in handling confidential and legally sensitive information.
Confident individual, able to work in a fast-paced environment and manage short-term deliverables along with long-term projects.
Excels when working as part of a team, collaborating effectively both within BSG Integrity and cross-functionally.

Nice To Haves

Experience with Airflow or a comparable orchestration and scheduling platform.
Experience with Databricks, including Spark, Delta Lake, or Unity Catalog.
Working knowledge of applied machine learning and experience integrating model inference into production applications.
Experience building LLM-based applications (retrieval, summarization, or agentic workflows), particularly for document-heavy or investigative use cases.
Familiarity with graph databases and graph-based data modeling; experience with Amazon Neptune or a comparable graph database is a plus.
Experience integrating open-source intelligence or third-party intelligence services into production applications.
Experience with data visualization libraries and dashboard design.
Prior work on integrity, surveillance, fraud, AML, or trust and safety systems is a plus.
Experience in sports, gaming, or another regulated or compliance-driven environment is a plus.

Responsibilities

Own the backend architecture that powers the Integrity platform – build, schedule, monitor, and maintain the services and automated jobs that support application functionality, model execution, and investigative workflows
Design and build the front-end UI the Integrity Team uses to review flagged activity, including alert queues, case views, market and player drill-downs, and supporting visualizations.
Partner with data scientists to integrate anomaly detection and machine learning models and outputs into production applications, including deployment, inference, versioning, and monitoring.
Integrate third-party and internal data services into the platform, defining service interfaces and application-level schemas and building resilience for late, missing, or malformed data.
Build application logic and services on top of curated warehouse datasets, partnering with the Analytics Engineer when new transformations or data models are required.
Improve reliability and observability across the stack — alerting, logging, error handling, performance monitoring, and service health — so that system failures are detected and resolved quickly.
Build audit-ready capabilities in partnership with the Legal department, including role-based access controls, evidence exports, and durable records of what the system surfaced and when.
Instrument feedback loops so that investigator dispositions and case outcomes improve system precision over time.
Support active investigations by rapidly developing application features, internal tools, or views needed by the Integrity Team and Legal department, occasionally on short timelines during the season.
Document the system clearly, write and maintain tests, participate in code review, and standardize conventions across the codebase to ensure long-term maintainability.