Streaming & EDA And Kafka Data Architect Role-6Months-London

Kirtana Consulting; Greater London; On-site; GBP 80,000 - 100,000; Full time

Job summary

A consultancy firm is seeking a Data Architect for a 6-month rolling contract in London. The role requires over 10 years of experience and expertise in Streaming & EDA, particularly with Kafka and AWS technologies. Key responsibilities include designing scalable data architectures, ensuring data governance, and implementing best practices for data management. Candidates should have a strong understanding of database technologies and data modeling principles. This position promises an exciting opportunity to work with cutting-edge data solutions in a dynamic environment.

Qualifications

• Over 10 years of experience in data architecture.
• Proficient in Kafka and AWS data stack.
• Strong understanding of data modelling and governance.

Responsibilities

• Design and implement data architectures supporting scalability.
• Ensure data integrity, quality, and compliance.
• Facilitate effective data governance practices.

Skills

Kafka (Confluent)

AWS MSK/Kinesis

EventBridge

Avro/Protobuf

AWS tools (S3, Glue, Athena)

Payments knowledge (ISO 20022)

Governance principles

Data architecture fundamentals

Tools

QuickSight

Tableau

Job description

Kirtana consulting is looking for Streaming & EDA and Kafka Data architect role for 6months rolling contract in London.

Job description:

Role Title: Data Architect

Minimum years of experience: >10 years

Must-Have Skills

- Streaming & EDA: Kafka (Confluent) and AWS MSK/Kinesis/Kinesis Firehose; outbox, ordering, replay, exactly/at-least-once semantics. EventBridge for event routing and filtering.
- Schema management: Avro/Protobuf + Schema Registry (compatibility, subject strategy, evolution).
- AWS data stack: S3/Glue/Athena, Redshift, Step Functions, Lambda; Iceberg-ready lakehouse patterns. Kinesis's3 Glue streaming pipelines; Glue Streaming; DLQ patterns.
- Payments & ISO 20022: PAIN/PACS/CAMT, life cycle modelling, reconciliation/advices; API/File/SWIFT channel knowledge.
- Governance: Data-mesh mindset; ownership, quality SLAs, access, retention, lineage.
- Observability & FinOps: Build dashboards, alerts, and cost KPIs; troubleshoot lag/throughput at scale.
- Delivery: Production code, performance profiling, code reviews, automated tests, secure by design.

Data Architecture Fundamentals (Must-Have)

- Logical data modelling: Entity-relationship diagrams, normalization (1NF through Boyce-Codd/BCNF), denormalization trade-offs; identify functional dependencies and key anomalies.
- Physical data modelling:Table design, partitioning strategies, indexes; SCD types; dimensional vs. transactional schemas; storage patterns for OLTP vs. analytics.
- Normalization & design: Normalize to 3NF/BCNF for OLTP; understand when to denormalize for queries; trade-offs between 3NF, Data Vault, and star schemas.
- CQRS (Command Query Responsibility Segregation):Separate read/write models; event sourcing and state reconstruction; eventual consistency patterns; when CQRS is justified vs. overkill.
- Event-Driven Architecture (EDA): Event-first design; aggregate boundaries and invariants; publish/subscribe patterns; saga orchestration; idempotency and at-least-once delivery.
- Bounded contexts & domain modelling: Core/supporting/generic subdomains; context maps (anti-corruption layers, shared Kernel, conformist, published language); ubiquitous language.
- Entities, value objects & repositories: Domain entity identity; immutability for value objects; repository abstraction over persistence; temporal/versioned records.
- Domain events & contracts: Schema versioning (Avro/Protobuf); backward/forward compatibility; event replay; mapping domain events to Kafka topics and Aurora tables.

Nice-to-Have

- QuickSight/Tableau; Redshift tuning; ksqlDB/Flink; Aurora Postgres internals.
- Edge/API constraints (Apigee/API-GW), mTLS/webhook patterns.