GraphDB Architect

Key Responsibilities

• Architecture ownership — define the end-to-end solution architecture across Ingest, Entity Resolution Engine and Serve workspaces on Microsoft Fabric; produce solution blueprints, architecture diagrams and integration patterns.
• Entity ontology & canonical model — design the entity ontology, canonical data model, attribute and provenance model, and the identifier spine
• Entity resolution strategy — define the matching approach: deterministic rules on shared identifiers, blocking strategy for candidate generation, probabilistic scoring features, confidence banding and survivorship rules.
• Graph architecture — design the graph schema (nodes, edges, properties), define relational-to-graph projection logic, model entity/ownership/affiliation relationships, and design incremental re-projection on CDC.
• Platform decisions — evaluate and select the graph and vector platform approach (Fabric-native Graph vs alternatives), with a supporting capacity, performance and cost model.
• Performance & capacity design — design Spark pool configuration and workspace/capacity strategy for compute-intensive resolution workloads; optimise Delta file sizes, partitioning and pipeline efficiency.
• Standards & governance — define data access policies (RBAC, Fabric security roles), data quality rules, metadata standards and naming/versioning conventions.
• Design assurance — review deliverables including code, models, pipelines and graph schemas; mentor engineers and ensure alignment to architectural standards.
• Client engagement — present and defend architecture decisions to WK stakeholders and Microsoft; support technical discovery and design workshops.

Required Skills & Experience

Skill Area

Specific Requirements

Graph Technology

Labeled property graph (LPG) modelling, graph query languages (GQL / Cypher / Gremlin), multi-hop traversal design, graph schema design, projection patterns

MDM

Deterministic and probabilistic matching, blocking strategies, survivorship and golden-record design, corporate hierarchy modelling, identifier spines

Microsoft Fabric

Lakehouse, Warehouse, OneLake, Data Factory, Spark/notebooks, Mirroring & CDC, capacity and workspace design, Fabric Graph

Architecture

Medallion / multi-zone lakehouse (Bronze→Silver→Gold), data modelling (dimensional, Data Vault), integration patterns, API design

AI / Retrieval

GraphRAG concepts, vector search and embeddings, natural-language-to-query approaches

Engineering Depth

Python/PySpark, SQL, Delta Lake, performance tuning, distributed compute optimisation

Leadership

Design authority, technical mentoring, client-facing architecture presentation, trade-off analysis and decision documentation

Must-Have Qualifications

• 12+ years in data engineering/architecture with at least 3 years designing graph or MDM solutions
• Hands-on architecture experience with graph databases and graph data modelling
• Demonstrable entity resolution / record linkage design experience at scale
• Deep Microsoft Fabric or equivalent modern lakehouse platform expertise
• Experience owning architecture decisions in a client-facing enterprise engagement
• Strong hands-on ability — this is a working architect role, not advisory only

Nice-to-Have

• Experience with Splink or comparable probabilistic linkage frameworks
• Exposure to GraphRAG or retrieval-augmented generation over knowledge graphs
• Background in corporate/legal entity, KYC, credit or compliance data domains
• Microsoft certifications (Fabric Analytics Engineer, Azure Solutions Architect)

Key Deliverables Owned

• Solution architecture and design documentation (HLD/LLD)
• Entity ontology and canonical data model
• Entity resolution strategy: match features, blocking and threshold design
• Graph schema and node/edge projection logic
• Graph & vector platform decision paper with capacity/cost model
• Architecture and data-flow diagrams; reusable component standards

Dual Role / Complementary Skills

Strong complementary overlap with the Entity Resolution strategy — this architect is expected to define the matching approach that the data engineering team implements. Can also act as interim technical lead for the Graph and Vector engineers during ramp-up, and is the natural escalation point for performance and capacity issues.