Vice President-LME Devops

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。