Vice President - LME Data Platform

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.

Key Responsibilities

Market Data & Data Product Enablement

• Collaborate with product/business stakeholders to define market data product objectives and translate them into data platform deliverables.
• Consolidate, normalise, and curate market data sets as governed, reusable data assets.
• Define data contracts, metadata, lineage, and quality rules for reliable market data product consumption.

Enterprise Data Management & Architecture

• Define and evolve enterprise data management architecture across data lake and data warehouse solutions.
• 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.

Big Data Engineering & Pipeline Delivery

• Design, build, and maintain scalable ETL/ELT pipelines for analytics and reporting.
• Implement and operate workflow orchestration frameworks such as Apache Airflow, Prefect, or Dagster.
• Engineer processing solutions using big data stacks such as Hadoop, Spark, Kafka, and Flink.

Data Stores, Query Performance & Reliability

• Design data storage and access patterns across data warehouses and databases.
• Drive query and pipeline performance tuning and improve SLAs/SLOs for critical datasets.
• Lead incident analysis and root-cause investigations for data-related issues.

Leadership & Delivery

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

Requirements

Education & Experience

• Degree in Computer Science, IT, Data Engineering, or related disciplines.
• Typically 12+ years of experience delivering enterprise data management and big data platforms.
• Proven experience leading delivery, making architecture decisions, and managing stakeholders.

Technical Skills

• Enterprise data management; big data projects; data lake and data warehouse design/operations.
• Big data tech stacks: Hadoop / Spark / Kafka / Flink.
• ETL orchestration: Airflow / Prefect / Dagster.
• Big data computing engines: Spark and/or Databricks.
• Database technologies: NoSQL / HBase / ClickHouse / Snowflake.

Programming Languages

• Proficiency in at least one language commonly used for data engineering, such as Python, Scala, or Java.

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.