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Join us as a Data Modeler/Architect - VP at Barclays, where you’ll drive innovation and creativity by developing forward-thinking, data-driven solutions. Your problem-solving mindset, determination, and collaborative spirit will be key to challenging the status quo and introducing new ideas. In this role, you’ll be at the Centre of our data strategy, transforming raw information into actionable insights that power smarter decisions and operational excellence. You’ll collaborate across teams to optimize data pipelines, data structure, enhance governance, and support regulatory compliance—making a real impact on how Barclays delivers value.
Job Responsibility:
Lead Markets end‑to‑end data architecture strategy, defining the vision and multi‑year roadmaps that align business goals, regulatory drivers, and enterprise standards
Govern Markets focused enterprise and domain data models, ensuring accuracy, re‑use, interoperability, and regulatory‑ready traceability across priority data domains
Provide architectural leadership for Markets data products and platforms, enabling high‑quality design, semantic consistency, and integration across cloud and on‑prem ecosystems
Drive and evolve data architecture governance, setting modelling, metadata, lineage, and interoperability standards, and ensuring compliance through review boards and enterprise forums
Act as senior adviser to CDOs, CIOs, COOs and technical leaders, guiding complex design decisions and influencing cross‑functional teams with clarity and strategic insight
Investigation and analysis of data issues related to quality, lineage, controls, and authoritative source identification
Execution of data cleansing and transformation tasks to prepare data for analysis
Designing and building data pipelines to automate data movement and processing
Development and application of advanced analytical techniques, including machine learning and AI, to solve complex business problems
Documentation of data quality findings and recommendations for improvement
Requirements:
Significant experience in data architecture or enterprise data modelling, ideally at a major bank or investment bank
A great understanding of banking products, risk frameworks, regulatory data requirements and financial markets data
Expertise in data modelling tools and frameworks with previous experience scaling models across global functions
Database Systems: Relational: Oracle, SQL Server, PostgreSQL, MySQL
Columnar: Amazon Redshift, Snowflake
NoSQL: MongoDB, Cassandra (for some use cases)
SQL and Scripting: Advanced SQL (DDL, DML, performance tuning)
PL/SQL or T-SQL
Python or Shell scripting for data validation and automation
Data Warehousing & ETL: Data warehousing concepts (Inmon, Kimball)