Vice President - LME Market Data

Location:

CN-Shenzhen-HyQ

Shift:

Standard - 40 Hours (China)

Scheduled Weekly Hours:

40

Worker Type:

Permanent

Job Summary:

Lead the design and delivery of LME market data platforms that consolidate multiple real-time and historical market data sources into scalable enterprise data assets for external and internal consumption. This role focuses on enterprise data management and big data engineering—building robust data lake/warehouse foundations, orchestrated ETL/ELT pipelines, and performant analytics access to support market data commercialisation and business intelligence.

Job Duties:

Key Responsibilities

Market Data & Data Product Enablement

• Partner with product/business stakeholders to define market data product objectives and translate them into data platform deliverables.
• Consolidate, normalise, and curate market data sets (including derivatives and order book datasets) as governed, reusable data assets.
• Define data contracts, metadata, lineage, and quality rules so downstream users can reliably consume market data products.

Enterprise Data Management & Architecture

• Define and evolve enterprise data management architecture across data lake and data warehouse solutions (on-prem and/or cloud).
• Design and operate data lake/warehouse layers using technologies such as ADLS, Amazon S3, Google Cloud Storage, Azure Synapse SQL, Snowflake, Amazon Redshift, or Google BigQuery.
• Set standards for data modelling, governance, security controls, retention, and lifecycle management aligned with organisational policies.

Big Data Engineering & Pipeline Delivery

• Design, build, and maintain scalable ETL/ELT pipelines for analytics and reporting using code-driven patterns and distributed compute engines.
• Implement and operate workflow orchestration frameworks such as Apache Airflow, Prefect ("Perfect"), or Dagster, including scheduling, dependency management, and observability.
• Engineer processing solutions using big data stacks such as Hadoop, Spark, Kafka, and Flink ("Flint"), ensuring throughput, reliability, and cost efficiency.
• Leverage Spark and/or Databricks (built on Spark) to deliver large-scale transformations and performance-tuned workloads.

Data Stores, Query Performance & Reliability

• Design data storage and access patterns across data warehouses and databases, including NoSQL stores (e.g., HBase) and analytical engines (e.g., ClickHouse, Snowflake).
• Drive query and pipeline performance tuning (partitioning, caching, file formats, indexing/cluster keys) and improve SLAs/SLOs for critical datasets.
• Lead incident analysis and root-cause investigations for data-related issues; implement permanent fixes and continuous reliability improvements.

Leadership & Delivery

• Operate effectively in a small, specialised team—balancing hands-on contribution with technical leadership, coaching, and setting engineering standards.
• Promote SDLC best practices, CI/CD, automated testing, monitoring, and documentation to improve delivery quality and repeatability.
• Coordinate with global engineering and infrastructure teams to deliver roadmap outcomes and manage dependencies.

Requirements

Education & Experience

• Degree in Computer Science, IT, Data Engineering, or related disciplines (or equivalent practical experience).
• Typically 12+ years of experience delivering enterprise data management and big data platforms; experience in financial services, exchanges, or regulated environments is advantageous.
• Proven experience leading delivery, making architecture decisions, and managing stakeholders across cross-functional teams.

Technical Skills (Key Words / Must-have)

• Enterprise data management; big data projects; data lake and data warehouse design/operations (e.g., ADLS, S3, GCS, Synapse SQL, Snowflake, Redshift, BigQuery).
• Big data tech stacks: Hadoop / Spark / Kafka / Flink.
• ETL orchestration: Airflow / Prefect / Dagster.
• Big data computing engines (code-driven ETL): Spark and/or Databricks.
• Database technologies: NoSQL / HBase / ClickHouse / Snowflake; strong SQL fundamentals and performance tuning.

Programming Languages

• Proficiency in at least one language commonly used for data engineering (e.g., Python, Scala, or Java).
• Java experience is beneficial but not mandatory; selection will be based on overall big data and enterprise data platform expertise.

Core Competencies

• Strong analytical and problem-solving skills; outcome-driven and able to prioritise under changing needs.
• Clear communication and stakeholder management across technical and non-technical audiences.
• Accountable, proactive, and comfortable operating in a lean team environment.

Company Introduction:

ITD SZ

港交所科技（深圳）有限公司，是2016年12月28日于深圳市前海自贸区成立的外商独资企业。

作为港交所的技术子公司，港交所科技（深圳）有限公司主要是为集团及其附属公司提供计算机软件、计算机硬件、信息系统、云存储、云计算、物联网和计算机网络的开发、技术服务、技术咨询、技术转让；经济信息咨询、企业管理咨询、商务信息咨询、商业信息咨询、信息系统设计、集成、运行维护；数据库管理、大数据分析；以承接服务外包方式提供系统应用管理和维护、信息技术支持管理、数据处理等信息技术和业务流程外包服务。