Analytics Engineer, Integrity

Global Sports Jobs Limited; New York (NY); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Annual discretionary bonus

Medical, dental, and vision insurance

Wellbeing and professional development

401(k) retirement plan

Job summary

Global Sports Jobs Limited is seeking an analytics engineer to own ingestion and transformation of data powering the Integrity Team’s policy work. You will normalise feeds from betting partners, OSINT, and vendors into a conformed model and integrate with internal basketball data for game context.

Lead production data models with dbt on Snowflake, implement data quality, and collaborate with data scientists and legal to support investigations and ML workflows.

Qualifications

• Advanced SQL and data modelling for analytical datasets.
• Experience with dbt, Snowflake, and scalable data pipelines.
• Proficiency in Python for ingestion and processing.
• Understanding of data warehousing, dimensional modelling, and governance.
• Experience with multi-source ingestion from vendors and OSINT.
• Strong communication skills and ability to translate requirements for legal and technical teams.

Responsibilities

• Own data ingestion from betting partners, OSINT, and vendors and normalise into a conformed model.
• Build OSINT ingestion pipelines and perform entity resolution to link identities.
• Integrate external data with internal basketball data for game context analysis.
• Develop production-ready dbt models on Snowflake with best practices.
• Create reusable datasets for data science and ML workflows.
• Implement automated data quality testing, monitoring, and reconciliation checks.
• Manage orchestration and scheduling for pipelines with dependency handling.
• Develop semantic layer with standard definitions and metrics.
• Document data sources, lineage, and assumptions for audits.
• Onboard new feeds and absorb schema changes without breaking downstream.
• Collaborate with data scientists and full-stack engineers to expose data assets.

Skills

Advanced SQL

dbt

Snowflake

Dimensional modelling

Python

Data integration

ETL pipelines

Git workflows

Stakeholder management

Communication

Education

Master’s degree in a technical or quantitative field

Tools

Databricks

Spark

Delta Lake

Unity Catalog

MLflow

Airflow

Dagster

Prefect

Job description

About the Role

The Basketball Strategy & Growth department is seeking an analytics engineer to own the ingestion and transformation of the data that powers the Integrity Team’s work on the league’s gaming policy. The role turns disparate and inconsistent external feeds — data from betting partners and operators, open-source intelligence (OSINT), and third-party vendors — into well-modelled, tested, and documented datasets, and integrates those feeds with internal league basketball data so that betting activity can be evaluated in the context of what happened on the court. These models are the foundation for downstream data science and machine learning work, including anomaly detection, alerting, and AI-driven software used to surface conduct that may violate the gaming policy. The analytics engineer joins a team of data scientists, engineers and basketball experts and collaborates closely with the Legal department on investigations relating to potential violations of the gaming policy; the work is confidential, frequently time-sensitive, and expected to hold up to legal scrutiny, with lineage, definitions, and data quality controls mattering as much as speed.

Key Responsibilities

• Own the ingestion of data from disparate betting partners, operators, and data vendors — each with its own schema, granularity, delivery cadence, and idiosyncrasies — and normalise them into a consistent, conformed model of markets, wagers, prices, and accounts.
• Build and maintain ingestion pipelines for OSINT and other unstructured or semi-structured sources, including the entity resolution work required to link external identities to known accounts and subjects.
• Integrate betting and OSINT data with internal league basketball data — schedule, play‑by‑play, box‑score, tracking, officiating, and player availability data — so that activity can be analysed in game context.
• Develop production‑ready analytical data models in dbt on Snowflake, applying dimensional modelling and analytics engineering best practices, including layered staging and mart designs, incremental models, and consistent naming conventions.
• Create curated, reusable pipelines and feature‑ready datasets that support downstream data science and machine learning work, including model development, backtesting, and production inference.
• Implement automated data quality testing, freshness and volume monitoring, and reconciliation checks on incoming feeds so that vendor changes, outages, or silent data loss are detected before they affect investigative output.
• Build and manage orchestration and scheduling for pipelines, including dependency management, retries, alerting, and service levels appropriate to time‑sensitive feeds.
• Develop and maintain a semantic layer with standardised business definitions and metrics so that analysts, data scientists, and the Legal department work from a single version of the truth.
• Document models, sources, lineage, and assumptions to support knowledge transfer, auditability, and the evidentiary needs of investigations.
• Onboard new betting partners and data providers — evaluating feed quality, defining requirements and specifications with providers, and absorbing vendor schema changes without breaking downstream consumers.
• Partner with data scientists to translate analytical and modelling requirements into scalable data models, and with the full stack engineer to expose those datasets to the platform’s backend jobs and UI.
• Apply appropriate access controls, data classification, and retention practices to sensitive betting, personal, and investigative data.

Required Experience & Qualifications

• Advanced SQL skills with demonstrated experience developing complex analytical data models.
• Production experience with analytics engineering frameworks such as dbt, including testing, documentation, macros, and managing a large model DAG.
• Production experience with Snowflake, including performance and cost management such as warehouse sizing, clustering, and query tuning.
• Strong understanding of dimensional modelling, data warehousing, and analytics engineering best practices.
• Understanding of sports betting markets — odds, line movement, limits, player props, and market maker behaviour — and of basketball data such as play‑by‑play, box‑score, and tracking data.
• Proficiency in Python for ingestion, API integration, and data processing.
• Experience integrating data from multiple external partners or vendors, including messy, high‑volume, semi‑structured, and unstructured sources.
• Experience building and operating orchestrated data pipelines, including scheduling, dependency management, retries, and monitoring.
• Experience developing semantic layers, standardised business definitions, and reusable analytical datasets.
• Experience implementing automated data quality testing, monitoring, and documentation to support trusted analytical data assets.
• Experience building pipelines that serve downstream data science and machine learning consumers.
• Familiarity with Git‑based development workflows (GitHub or Azure DevOps), code review, and CI practices.
• Strong communication and stakeholder management skills, with the ability to translate business and investigative requirements into scalable analytical solutions for both technical and non‑technical audiences, 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.

Preferred Skills

• Master’s degree preferred in a technical or quantitative field.
• Experience with Databricks, including Spark, Delta Lake, Unity Catalog, and/or ML lifecycle tooling such as MLflow.
• Experience with Airflow, Dagster, Prefect, or a comparable orchestration platform.
• Experience with entity resolution, identity matching, or record linkage.
• Familiarity with MLOps practices such as feature pipelines, model registries, training/retraining workflows, and model monitoring.
• Prior work on integrity, surveillance, fraud, AML, or trust and safety data is a plus.
• Experience in sports, gaming, or another regulated or compliance‑driven environment is a plus.

Workplace

The NBA currently provides eligible employees the option of working remotely one day per week.

Onsite work in the New York and New Jersey offices requires full COVID‑19 vaccination; the NBA will discuss accommodations for individuals who cannot be vaccinated due to a medical reason or sincerely held religious belief, practice, or observance.

Benefits & Perks

• Employees and/or eligible dependents may be eligible to participate in Company-sponsored employee benefit programmes.
• Annual discretionary performance bonus, awarded at the Company’s sole discretion and subject to any terms and conditions set by the Company.
• Medical, dental, and vision insurance.
• Life/AD&D insurance and short- and long‑term disability insurance.
• Fertility and family‑forming assistance.
• Wellbeing allowance.
• Educational assistance.
• Mental health coaching/therapy.
• Tax‑advantaged accounts such as HSA and healthcare/dependent care FSAs.
• 401(k) retirement plan.
• Time off benefits that include vacation, sick time, and personal days.