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 for data 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

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:

Platform / Store

• Primary Use in

• 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:

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