Data Architect – Payments & Streaming (Contract)
Contract: 6 months
Work mode: Hybrid (2–3 days WFO)
Location: London
Vacancies: 2
Experience: 10+ years
Role Overview
We are seeking a senior Data Architect with strong data product architecture experience to design and deliver scalable, event-driven data platforms within a payments domain. The role requires deep expertise in streaming, AWS-native data services, and ISO 20022 message models, combined with strong data modeling fundamentals and production-grade delivery skills.
Key Responsibilities
Own data product architecture end-to-end, from domain modeling to production deployment.
Design and implement event-driven architectures using Kafka (Confluent) and AWS streaming services.
Define and govern data contracts, schemas, and evolution strategies.
Architect AWS lakehouse and streaming pipelines (real-time and batch).
Model payments lifecycles and reconciliation flows aligned with ISO 20022.
Apply CQRS, event sourcing, and domain-driven design where appropriate.
Establish data governance standards aligned to a data-mesh mindset.
Ensure observability, reliability, and cost efficiency of data platforms.
Produce production-quality code, conduct reviews, and drive engineering best practices.
Must-Have Skills
Streaming & Event-Driven Architecture
Kafka (Confluent) and AWS MSK / Kinesis / Kinesis Firehose
Event ordering, replay, idempotency, DLQs
Exactly-once vs at-least-once semantics
EventBridge for routing and filtering
Saga patterns and eventual consistency
Schema & Contract Management
Avro / Protobuf
Schema Registry (compatibility modes, subject strategy, evolution)
Domain events mapped to Kafka topics and persistence stores
AWS Data Architecture
S3, Glue (batch & streaming), Athena
Redshift (analytics)
Lambda, Step Functions
Iceberg-ready lakehouse patterns
Streaming ingestion: Kinesis → S3 → Glue
Error handling and DLQ patterns
Payments & ISO 20022
PAIN / PACS / CAMT message types
Payment lifecycle modeling
Reconciliation and advice flows
API, file-based, and SWIFT channel knowledge
Data Architecture Fundamentals
Logical data modeling (ERDs, normalization 1NF → BCNF)
Physical data modeling (partitioning, indexing, SCDs)
OLTP vs analytical storage patterns
3NF vs Data Vault vs Star Schema trade-offs
CQRS design and justification
Bounded contexts and domain modeling
Entities, value objects, repositories
Temporal and versioned data models
Governance & Operations
Data mesh principles (ownership, SLAs, access, retention, lineage)
Observability (lag, throughput, failures)
FinOps KPIs and cost optimization
Secure-by-design data platforms
Nice-to-Have
QuickSight or Tableau
Redshift performance tuning
ksqlDB or Flink
Aurora PostgreSQL internals
API Gateway / Apigee, mTLS, webhook patterns