Manager, Racing Data and AI Engineering at The Hong Kong Jockey Club

Hong Kong, Hong Kong Island, Hong Kong S.A.R. -

Full Time

Start Date

Immediate

Expiry Date

03 May, 26

Salary

0.0

Posted On

02 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Data Integration, ETL Processes, SQL, Python, Data Modeling, AI Techniques, Predictive Modeling, Data Governance, Analytics Dashboard Development, IoT Data, Biometric Data, Automation Tools, Data Pipelines, Data Quality, Collaboration

Industry

Non-profit Organizations

Description

The Hong Kong Jockey Club Founded in 1884, The Hong Kong Jockey Club (“the Club”) is a world-class racing club that acts continuously for the betterment of our society. The Club has a unique integrated business model, comprising racing and racecourse entertainment, a membership club, responsible sports wagering and lottery, and charities and community contribution. Through this model, the Club generates economic and social value for the community and supports the HKSAR Government in combatting illegal gambling. The Division The Racing Division is committed to managing all aspects of Hong Kong’s world-class sport of horse racing, covering the full value chain — from importation and training to racedays, equestrian, welfare, integrity, and international engagement. With Conghua Racecourse and Greater Bay Area expansion, we’re shaping racing into a dynamic sporting, social, and entertainment experience. Job Summary This role supports the Racing Division in rationalizing and managing data assets across diverse sources to enable high‑quality analytics use cases. It leads the design and operation of a robust, scalable data architecture and automated data pipelines that transit prototypes into BAU, accelerate time‑to‑market, expand self‑service analytics, and deliver trusted, high‑quality data for evolving business needs. The role works across relational databases, object storage, IoT tracking sensors, biometric data etc, and applies AI techniques to convert unstructured inputs into structured features for analytics and predictive modelling: Data Integration & Management: Design and implement scalable data models and architecture for timely dashboard updates, self-service analytics, and a unified horse view across systems. Develop and maintain data pipelines: Collect, clean, and integrate data from multiple sources to consolidate and transform Racing data into the AI platform and Racing Analytics Data Mart, ensuring quality and reliability. Support Analytics Dashboard Development: Design, build, and maintain data models for interactive dashboards for various use cases in the Racing Division. Collaborations with data scientists on AI projects: Build and manage feature stores for training predictive and AI models, enabling advanced insights and decision-making in Racing Division. Support Management to establish and enforce data governance i.e. data quality, data lineage, metadata management, compliance and develop data management tools ensuring integrity across data assets. Apply Racing domain knowledge to support actionable insights delivery examples including race programming, prize money strategy, field-size and horse population forecast, injury predictions etc; prepare materials to present findings for Senior Managment Team Meetings. Ad‑hoc Analytics: Provide on‑demand exploratory data analyses to generate actionable insights for racing teams when required. Documentations: Maintain clear documentation of data definitions, business logic, and analytical methodologies. Serve as Racing data SME and ensure compliance with data quality and integrity standards. Racing Systems Revamp & Modernization: Contribute to the data modeling workstream, define landing/curated layers for analytics, plan migrations with minimal disruption and conduct UATs. Project Engagement and Management: Independently manage engagement and collaboration with IT on analytics initiatives; contribute to cross‑functional projects with advanced data modelling and analysis expertise. The Job Support Analytics Use Cases Prototyping & Industrialization to BAU Rapid Prototyping: Partner with analysts/data scientists to stand up pilot use cases using sandboxed data pipelines and experimental schemas. Production Hardening: Convert prototypes into production-grade data products Industrialization Playbook: Define and enforce “pilot-to-prod” readiness criteria and manage change through release governance. Operational Handover: Transition solutions into BAU with monitoring dashboards. Lifecycle Management: Establish versioning, deprecation processes, and periodic reviews to keep BAU pipelines aligned with evolving KPI definitions. Data Engineering and Management Data Requirement Elicitation: Engage with stakeholders to translate business/analytics requirements into data requirements (schemas, SLAs, lineage, retention, privacy/security). Implementation & Orchestration: Build robust ETL/ELT pipelines (APIs, databases, flat files, IoT/sensor streams), model data, and orchestrate jobs automation tools with CI/CD. Operational Monitoring: Continuously track pipeline health, publish operational status dashboards, and manage incidents to meet SLAs. Quality Assurance & Risk Management: Implement automated data validation and testing, enforce version control. Data Modelling & Standards: Define and maintain data dictionaries, schemas, and data contracts; standardize formats/units and implement Change Data Capture (CDC) where needed. Unified Data: Consolidate multi-source data into a resilient, shareable platform (lake/warehouse/lakehouse), ensuring consistent identifiers and referential integrity. KPI Dashboards Enablement Build Data Marts for KPI Reporting: Design, build, and maintain curated data marts/semantic layers powering KPI dashboards for various racing business teams and operations. Consistent Definitions: Document and govern KPI measures, business definitions, and transformation logic to ensure consistency across reports and teams; manage role-based access to curated datasets. Performance Optimization: Implement aggregations, indexing/partitioning, materialized views, and query optimization for fast, reliable dashboard performance. Support Predictive Modelling (DataOps/MLOps) Feature Stores & Training Data: Provision reproducible, versioned feature sets and training datasets; manage data lineage and metadata for models Training/Scoring Pipelines: Build model data pipelines for batch and streaming inference; integrate with model registries and monitoring. Quality, Latency, Observability: Ensure data pipelines meet accuracy and timeliness targets for modelling; log inputs/outputs for model monitoring and compliance. Delivery & UAT: Collaborate with delivery teams to develop, test, and deploy solutions; coordinate documentation and execute UAT for data components. Documentation, Testing, and Compliance Technical Documentation: Maintain architecture diagrams, pipeline runbooks, schema registries, and lineage/crosswalks. User-Facing Docs: Publish data dictionaries, semantic model definitions, and onboarding guides for analysts and dashboard developers. Testing & Change Management: Define UAT plans for data deliverables, manage feedback loops, and govern changes to schemas and pipelines. Cross-Functional / Cross-Departmental Projects Contribute to the Racing Systems Revamp & Modernization programme with data modelling expertise. Integration & Scalability: Support initiatives requiring integration of business performance and equine data; design solutions that align with the Club’s standards. About You Bachelor’s/Master’s in Computer Science, Data, Software Engineering, Information Systems, or related field. 3-5 years of experience in data engineering or analytics platform roles, with production‑grade delivery of data products and pipelines. Proven experience operationalising prototypes into BAU and scaling platforms for multiple teams/use cases. Experience with IoT data, biometric data, and unstructured data processing using AI tools is an advantage. Understanding of the Racing industry is an advantage but not essential. Strong hands-on experience in SQL, Python, and distributed processing (e.g. PySpark/Databricks) Strong understanding of data integration and ETL processes Experience in managing and working with multiple types of databases and systems, e.g. relational databases relational databases, object storage and big data platforms (e.g. Databricks) Experience with automation tools (e.g. Ansible) and orchestration of data pipelines and data lake/warehouse architecture (e.g. medallion architecture). Experience in designing robust data models for self-service analytics on BI tools (e.g. Tableau) Ideally experience with streaming data and APIs Hands-on with building feature stores, MLflow/MLOps, and BI tools Tableau, Power BI or similar). Familiarity with IoT data, biometric data, and unstructured data processing using AI tools is an advantage. Experience with designing or automating workflows using AI Agents with AI orchestration platforms (such as MS Copilot Studio or N8N) will be considered an advantage. Governance & Security: Data catalogues/lineage, role‑based access control, PII handling, encryption, compliance. Domain knowledge in Racing business and related analytics is an advantage. Strong skills with Excel, Word, and PowerPoint Excellent communication and presentation skills Results-driven and ability to manage multiple projects Strong interpersonal and time-management skills Fluency in both written and spoken English Fluency in Mandarin and/or Cantonese is an advantage but not essential. Apply Now! We offer competitive salary and benefits packages, dynamic working environment and development opportunities. Add horsepower to your career today. Click the “Apply Now” button to create an account and submit your application. Equal Opportunity and Inclusive Hiring We are an equal opportunity employer and strive to create an inclusive workplace for all. Applicants from diverse backgrounds are welcomed to apply. If you have any special needs or require accommodations during the interview process, please e-mail us via careers@hkjc.org.hk. Personal data provided by job applicants will be used strictly in accordance with the Club's notice to employees and job applicants relating to the Personal Data (Privacy) Ordinance. A copy of which will be provided immediately upon request.

Responsibilities

The role involves managing data assets to enable high-quality analytics and leading the design of data architecture and automated pipelines. It also includes collaborating with data scientists on AI projects and supporting management in establishing data governance.