Data Architect

Esure Group PLC; Milton Keynes; On-site; GBP 50,000 - 75,000; Full time

Job summary

An innovative insurance company in Milton Keynes is on the lookout for a Data Architect to enhance their dynamic Data department. This role involves designing and managing scalable cloud data platforms and integrating diverse data sources for analytics. The ideal candidate will have proven expertise in data modelling, experience with data pipelines, and a strong understanding of SQL. Join a team dedicated to shaping the future of analytics and AI-driven decision-making in the insurance sector.

Qualifications

• Proven expertise in data modelling with complex dimensional models.
• Strong background in building scalable data structures.
• Extensive experience with fact and dimension tables across various domains.

Responsibilities

• Design and manage scalable cloud data platforms.
• Integrate and model data from diverse sources.
• Enable AI and ML use cases with data pipelines.

Skills

Data modelling

Building data pipelines

SQL

Dimensional modelling

Governance frameworks

Education

Proven hands-on expertise in designing complex models

Tools

Databricks

AWS

Kafka

DBT

Job description

Data Architect

We are currently looking for a Data Architect to join our forward-thinking Data department. You'll work in a team of data engineers, analytics engineers, data scientists and AI specialists to design and evolve scalable data platforms and modern data products that enable self-service analytics, advanced modelling, and AI-driven decision-making across our insurance business.

• Design and manage scalable cloud data platforms (Databricks on AWS) across development, staging, and production environments, ensuring reliable performance and cost efficiency.
• Integrate and model data from diverse sources—including warehouses, APIs, marketing platforms, and operational systems—using DBT, Delta Live Tables, and dimensional modelling to deliver consistent, trusted analytics.
• Enable advanced AI and ML use cases by building pipelines for vector search, retrieval-augmented generation (RAG), feature engineering, and model deployment.
• Ensure security and governance through robust access controls, including RBAC, SSO, token policies, and pseudonymisation frameworks.
• Develop resilient data flows for both batch and streaming workloads using technologies such as Kafka, Airflow, DBT, and Terraform.
• Shape data strategy and standards by contributing to architectural decisions, authoring ADRs, and participating in reviews, data councils, and platform enablement initiatives.

**Qualifications**

• Proven, hands‑on expertise in data modelling with a strong track record of designing and implementing complex dimensional models, star and snowflake schemas, and enterprise-wide canonical data models.
• Proficiency in converting intricate insurance business processes into scalable and user-friendly data structures that drive analytics, reporting, and scenarios powered by technology.
• Extensive experience designing fact and dimension tables across domains such as policy, quote, claims, pricing, and fraud, ensuring consistency and alignment with business metrics.
• Deep practical knowledge of semantic layer design and implementation using DBT, SQL, and Delta Live Tables.
• Strong background in building high-performance, scalable data models that support self-service BI and regulatory reporting requirements.
• Direct exposure to cloud‑native data infrastructures (Databricks, Snowflake) especially in AWS environments is a plus.
• Experience in building and maintaining batch and streaming data pipelines using Kafka, Airflow, or Spark.
• Familiarity with governance frameworks, access controls (RBAC), and implementation of pseudonymisation and retention policies.
• Exposure to enabling GenAI and ML workloads by preparing model-ready and vector-optimised datasets.
• Demonstrated capability to collaborate successfully with interested parties in data engineering, analytics, architecture, and business teams.
• Advanced SQL skills and a keen focus on performance tuning, data integrity, and reusable compose patterns across data products.

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• Deep expertise in SIEM platforms (ArcSight preferred).
• Log processing technologies (Logstash or similar).
• Security data architecture design and implementation.

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