Data Architect

Bounteous; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A financial services consultancy in Greater London is seeking candidates with strong knowledge in Change Data Capture and Db2 & z/OS. Ideal applicants should have a robust understanding of data architecture fundamentals, including logical and physical data modeling. Responsibilities include modernizing legacy systems and ensuring data quality. This position offers an excellent opportunity to apply your financial services experience in a supportive environment.

Qualifications

• Financial services background (banking, payments, capital markets).
• Experience with modernizing legacy systems and production support.
• Knowledge in functional dependencies and anomalies in data.

Skills

Change Data Capture

Db2 & z/OS knowledge

Integration patterns

Data quality mindset

Logical data modeling

Physical data modeling

Normalization & design

Domain-Driven Design

Event-driven architecture

CQRS patterns

Database internals

Data quality & validation

Job description

Ideal Background

• Financial services background (banking, payments, capital markets)
• Mix of mainframe and modern technology experience
• Track record of modernizing legacy systems
• Production support and incident management experience

Must-Have

• Change Data Capture: CDC design and operations (IBM, Precisely, or equivalent); subscription management, bookmarks, replay, backfill.
• Db2 & z/OS knowledge: Db2 catalog, z/OS fundamentals, batch windows, performance considerations.
• Integration patterns: Kafka/MSK hands-on, CDC-to-target pipelines, UPSERT/MERGE logic; Python/SQL; strong troubleshooting.
• Data quality mindset: Write validation tests before migration; golden-source reconciliation.

Data Architecture Fundamentals (Must-Have)

• Logical data modeling : Entity-relationship diagrams, normalization (1NF through Boyce-Codd/BCNF), denormalization trade-offs; identify functional dependencies and anomalies.
• Physical data modeling: Table design, partitioning strategies, indexes; SCD types; dimensional vs. transactional schemas; storage patterns for OLTP vs. analytics.
• Normalization & design: Normalize to 3NF/BCNF for transactional systems; understand when to denormalize for queries; trade-offs between 3NF, Data Vault, and star schemas.
• Domain-Driven Design: Bounded contexts and subdomains; aggregates and aggregate roots; entities vs. value objects; repository patterns; ubiquitous language.
• Event-driven architecture: Domain events and contracts; CDC as event streams; idempotency and replay patterns; mapping Db2 transactions to event-driven architectures; saga orchestration.
• CQRS patterns: Command/query separation; event sourcing and state reconstruction; eventual consistency; when CQRS is justified for mainframe migration vs. overkill.
• Database internals: Index structures (B-tree, bitmap, etc.), query planning, partitioning strategies; how Db2 vs. PostgreSQL differ in storage and execution.
• Data quality & validation: Designing test suites for schema conformance; referential integrity checks; sampling and reconciliation strategies.

Nice-to-Have

None listed.