Senior Data Architect

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs. Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals. Role Overview We are seeking a Senior Data Architect to own and drive the Group-wide data architecture across MultiBank's core source systems — Trading, Back Office (BO), Introducing Broker (IB), CRM, Client Services (CS), Finance, Operations and the Application teams. This role is responsible for designing and evolving the Group's data platform end to end — from source system integration through data modelling, storage, and consumption — across AWS (or other cloud providers) and on-premises environments. You will define the target-state architecture, set data modelling and integration standards, and work closely with data engineers, application teams and the Data Governance function to ensure the platform is scalable, secure, well-governed and built for reuse. This is a senior technical role at the intersection of architecture, engineering and governance. You will set architectural direction, review and guide design decisions across initiatives, and partner with senior stakeholders across the Group. Key Responsibilities Data Architecture & Platform Design • Define and own the Group-wide data architecture strategy, principles, standards and reference architectures. • Design the enterprise data platform spanning ingestion, storage, processing, modelling and consumption layers, across cloud (AWS or other) and on-premises environments. • Architect the data lake / Lakehouse and data warehouse layers, including zoning (raw, curated, semantic), storage formats and access patterns. • Build and maintain the Group's architecture roadmap; track and report progress to leadership. Data Modelling & Database Design • Own conceptual, logical and physical data modelling across operational and analytical systems (OLTP and OLAP). • Design and review database schemas, dimensional models (star/snowflake), and data marts to support trading, finance, CS and operational analytics. • Set standards for data models, entity relationships, naming conventions and the enterprise data dictionary, working with Data Governance on the business glossary and metadata. • Evaluate and select appropriate database technologies (relational, NoSQL, time-series, columnar) for each use case. Data Integration & Engineering Oversight • Design end-to-end data integration patterns (batch, CDC, streaming, API-based) across Trading, BO, IB, CRM, CS, Finance and Operations source systems. • Set engineering standards and best practices for ETL/ELT pipelines, orchestration, data quality checks and error handling; provide technical guidance to the data engineering team. • Review and approve high-level designs for new integrations and data products so they are architected for reuse, performance and maintainability. • Champion a single, authoritative and well-integrated data foundation — a "single version of truth" — across functions. Cloud & Infrastructure • Architect and guide the use of AWS services (e.g. S3, Glue, Redshift, RDS/Aurora, Lambda, EMR, Kinesis) and equivalent services on other cloud platforms for data storage, processing and orchestration. • Define architecture for hybrid scenarios spanning cloud and on-premises databases and systems, including secure connectivity and data movement. • Own decisions on scalability, performance, cost-optimization and resilience of the data platform infrastructure. • Evaluate and recommend data platform tooling (warehousing, catalogue, orchestration, BI) in partnership with the Head of Data Platform. Governance, Quality & Security (Architectural Alignment) • Partner closely with the Data Governance function to ensure data models, lineage and metadata are captured and kept current as the architecture evolves. • Embed data quality, privacy and security requirements (including handling of PII, financial and regulatory data) into architectural designs by default. • Support regulatory, audit and reporting needs by ensuring the platform architecture provides accurate lineage, traceability and access controls. Leadership & Collaboration • Provide technical leadership and mentorship to data engineers and other members of the data platform team. • Act as the senior technical point of contact for data architecture with application teams, infrastructure, risk, compliance and business stakeholders. • Represent data architecture in roadmap, budget and vendor/tooling discussions. • Promote architectural best practices and drive adoption of standards across delivery teams. Role Requirements Architecture & Technical • Proven track record designing and delivering enterprise data architecture, ideally in financial services, brokerage or banking. • Strong hands-on expertise in data modelling (conceptual, logical, physical), dimensional modelling and database design for both OLTP and OLAP workloads. • Deep knowledge of relational databases (e.g. PostgreSQL, SQL Server, Oracle, MySQL) and exposure to NoSQL / other database technologies (e.g. MongoDB, Cassandra, DynamoDB). • Strong experience with cloud data platforms — AWS (Redshift, S3, Glue, RDS/Aurora, Lambda, EMR, Kinesis, Athena) preferred; familiarity with Azure, GCP or other clouds is a plus. • Experience designing on-premises and hybrid data architectures, including migration paths from on-premises to cloud. • Hands-on or strong working knowledge of data integration and ETL/ELT tools and patterns (batch, CDC, streaming — e.g. Kafka, AWS DMS/Glue, Informatica, Talend, Airflow, dbt). • Strong SQL skills and working knowledge of data warehousing platforms (e.g. Snowflake, Databricks, Redshift). • Familiarity with BI and reporting environments (Power BI, AWS QuickSight, Metabase or similar). Governance & Domain Knowledge (Value-Add) • Working knowledge of data governance frameworks and disciplines (e.g. DAMA-DMBOK) — ownership, stewardship, metadata, glossary, lineage and data quality — and how architecture supports them. • Domain knowledge of CFD / trading / financial services data is a strong value add — trades, orders, positions, P&L, payments/PSPs, client lifecycle and transaction flows. • Exposure to data governance / catalog tools such as Collibra, Informatica, Alation or Microsoft Purview is beneficial. • Awareness of regulatory and compliance considerations relevant to financial services da