Data Architect

Roles: Data Architect

Location: London, UK

Is it Permanent/ Contract: Open for both

Is it Onsite/Remote/Hybrid: for London (4 days WFO, 1-day WFH mandatory)

Experience

15+ years

About the Role:

We are seeking a senior Data Architect to join the engineering organisation as part of Project Compass. This programme is delivering next-generation capabilities across Accounts (Real-Time Ledger), Payments Engine, and Foreign Exchange – all of which generate, consume, and depend on high-quality, well-governed data at scale.

The Data Architect will own the end-to-end data architecture across spanning Snowflake as the enterprise data warehouse and a landscape of in-house application databases (relational, time-series, document, and in-memory stores) that serve real-time operational workloads. You will define how data flows from source systems into the warehouse, how application databases are modelled and managed, and how data products are exposed to downstream consumers within and beyond .

This is a hands-on, delivery-focused role. You will work closely with Integration Architects, platform engineers, and domain product teams to translate business data requirements into durable, governed, and scalable data solutions.

Key Responsibilities

Data Architecture & Strategy

• Define and own the data architecture target state, covering the Snowflake enterprise data warehouse, application databases, and the data flows that connect them
• Establish a unified data modelling standard across relational (PostgreSQL, Oracle), in-memory (Redis), time-series (TimescaleDB / InfluxDB), and document (MongoDB) stores used by applications
• Design the data ingestion and movement architecture – real-time CDC pipelines, batch ETL/ELT patterns, and event-driven feeds from the NATS messaging layer into Snowflake
• Define data domain boundaries, ownership, and lineage standards aligned with Project Compass product domains (RTL, Payments, FX)
• Produce and maintain authoritative data architecture artefacts: entity-relationship models, data flow diagrams, data dictionaries, and Architecture Decision Records (ADRs)

Snowflake & Data Warehouse

• Lead the design and evolution of the Snowflake data warehouse, including schema design (Raw / Conformed / Consumption layers), virtual warehouse sizing, and cost governance
• Define standards for data loading (Snowpipe, Streams & Tasks, external stages), transformation (dbt patterns), and data sharing across business units
• Establish Snowflake data access controls, row-level security, dynamic data masking, and PII governance in line with regulatory requirements (GDPR, BCBS 239)
• Champion Snowflake best practices for performance tuning, clustering keys, materialised views, and query optimisation
• Evaluate Snowflake-native capabilities (Snowpark, Cortex AI, Dynamic Tables) and recommend adoption where they accelerate data product delivery

Application Database Architecture

• Govern the application database landscape across – reviewing schema designs, indexing strategies, and data lifecycle management across all in-house databases
• Define patterns for operational data stores (ODS) that bridge real-time application databases and the analytical warehouse layer
• Ensure consistency between transactional data models and their warehouse representations, minimising transformation complexity and maximising fidelity
• Set standards for database change management, migration tooling (Liquibase / Flyway), and schema versioning across the application estate
• Identify and remediate data quality issues at source, defining data contracts between application teams and downstream consumers

Data Governance & Quality

• Define and implement data governance frameworks covering data ownership, stewardship, classification (PII, sensitive, public), and retention policies
• Establish data lineage and cataloguing standards, working with tooling such as Apache Atlas, Collibra, or Snowflake Horizon Catalog
• Design and enforce data quality rules and SLAs at ingestion, transformation, and consumption layers
• Collaborate with the Risk and Compliance function to ensure data architecture meets BCBS 239 Risk Data Aggregation and Reporting requirements
• Champion Master Data Management (MDM) principles for shared reference data (counterparty, instrument, currency) across domains

AI, Analytics & Data Products

• Define the architecture fordata products – curated, well-documented datasets served to analytics, reporting, and AI/ML consumers
• Design feature stores and data pipelines that support AI/ML model training and inference for use cases such as FX pricing, payment anomaly detection, and limit utilisation forecasting
• Evaluate and integrate AI-assisted data tooling (AI-powered cataloguing, natural language querying, automated data quality) where it accelerates productivity
• Partner with the Analytics Engineering team to establish dbt modelling standards, testing frameworks, and documentation practices

Collaboration & Leadership

• Work hands-on across multiple product teams as a data authority, balancing strategic design with direct delivery contribution
• Guide and mentor application engineers on data modelling, query optimisation, and data quality best practices
• Engage senior stakeholders across Technology, Finance, Risk, and Operations to communicate data strategy, risks, and trade-offs
• Facilitate data architecture working groups with platform, BI, and enterprise architecture teams to align on shared standards

Core Technical Skills

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Data Warehouse

Snowflake – schema design, Snowpipe, Streams & Tasks, Snowpark, dynamic data masking, cost governance

Transformation

dbt (data build tool) – modelling layers, testing, documentation, incremental strategies

Application Databases

PostgreSQL, Oracle, Redis, MongoDB, TimescaleDB / InfluxDB – schema design, indexing, replication

Data Integration

CDC (Debezium / Kafka Connect), ETL/ELT pipelines, NATS event feeds, AWS Glue, Apache Spark

Cloud Platform

AWS – S3, RDS, Aurora, Redshift (migration context), Glue, Lake Formation, IAM, VPC

Data Governance

Data lineage, cataloguing (Apache Atlas / Collibra / Snowflake Horizon), GDPR, BCBS 239, MDM

Architecture Practice

ERDs, data flow diagrams, data contracts, ADRs, C4 modelling, domain-driven data design

AI / ML Data

Feature stores, ML pipeline data design, Snowflake Cortex AI, vector stores, LLM data patterns

Query & Performance

SQL optimisation, clustering keys, partitioning, query profiling, cost-based tuning

Data Landscape

The Data Architect will work across the following technology landscape. Candidates should have direct experience with the majority of these platforms and the ability to define coherent architecture across heterogeneous stores:

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Platform / Store

Primary Use :

Key Architecture Concerns

Snowflake

Enterprise data warehouse, analytics, reporting, data sharing

Layer design, ingestion patterns, security, cost governance

PostgreSQL

Transactional data – ledger entries, client records, audit

Schema design, CDC, replication lag, index strategy

Oracle DB

Legacy core banking integration, reference data

Migration strategy, data contracts, schema versioning

Redis

Real-time caches – FX rates, limit state, session data

Cache invalidation, persistence strategy, data consistency

MongoDB

Document stores – client profiles, trade enrichment data

Schema evolution, aggregation pipelines, CDC integration

TimescaleDB

Time-series – market data ticks, position history

Hypertable design, retention policies, compression

NATS JetStream

Event streaming – payments, ledger events, FX confirmations

Event schema contracts, consumer group design, replay strategy

AWS S3 / Glue

Data lake staging, archival, batch ingestion into Snowflake

Partitioning, file format (Parquet/ORC), Lake Formation governance

Finance Domain Knowledge

Candidates should have hands-on data architecture experience in one or more of the following financial services domains:

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Domain

Key Data Concepts

Real-Time Ledger

Double-entry accounting data models, event-sourced ledgers, real-time balance aggregation, reconciliation datasets

Payments Engine

Payment message data (ISO 20022 / SWIFT), settlement instructions, payment status lifecycle, fee and charge data

Foreign Exchange

Trade data models, rate feeds and time-series storage, position keeping, P&L attribution data

Limit Management

Exposure data models, limit hierarchy, breach event data, real-time risk aggregation feeds

Client Onboarding

Client master data, KYC / AML data structures, account hierarchy, regulatory reporting feeds

Regulatory Reporting

BCBS 239 data lineage, EMIR / MiFID trade reporting data, data quality SLAs for regulatory submissions

Experience & Profile

• 15+ years of progressive technology experience, with at least 5 years in senior data architecture roles
• Deep, hands-on experience with Snowflake as an enterprise data warehouse – ideally holding Snowflake SnowPro Core or Advanced: Architect certification
• Proven track record of designing data architectures across heterogeneous application database landscapes in large financial institutions or fintech organisations
• Demonstrated experience implementing data governance frameworks, lineage tooling, and data quality programmes at programme scale
• Comfortable working hands-on – writing dbt models, reviewing SQL, profiling queries – while operating at senior stakeholder and architecture level
• Experience with CDC-based real-time data pipelines and event-driven data integration patterns
• Strong communicator able to convey complex data architecture decisions to both engineering teams and business stakeholders
• Familiarity with AI/ML data architecture patterns (feature stores, vector databases, LLM data pipelines) is a strong advantage
• AWS Solutions Architect or AWS Data Analytics certification is advantageous.