Data Engineering Tech Lead (MDM)

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As a Data Engineering Technical Lead you will play a critical leadership role in shaping Aviva’s enterprise data foundations, ensuring master and reference data is trusted, governed, scalable, and reusable across the organisation

This role is responsible for leading the engineering and delivery of a cloud-native Master & Reference Data platform (MDM/RDM), enabling consistent, high-quality data across core business domains such as Customer, Product, Supplier and other master entities across Aviva.

You will lead a cross-functional engineering team to build scalable, event-driven data pipelines, mastering workflows, and API-driven data services using Kafka (MSK), AWS services, and containerised microservices (Spring Boot / Python). This is a hands-on leadership role focused on delivering high-quality engineering outcomes while embedding strong governance, data quality, and standardisation practices.

What will you be doing?

You will be responsible for:

Technical Leadership & Team Guidance

• Lead and mentor a team of data and backend engineers aligned to MDM/RDM delivery

• Translate enterprise data architecture and domain models into implementable designs

• Drive engineering best practices including coding standards, CI/CD, and quality assurance

• Act as the primary technical escalation point for MDM platform delivery

MDM/RDM Platform Engineering & Delivery

Guide the development of:

• Mastering and matching workflows (e.g., survivorship, deduplication, golden record creation)

• Kafka / MSK-based event-driven data pipelines for master data ingestion and distribution

• AWS Glue ETL jobs (Python/PySpark) for data standardisation and enrichment

• Containerised microservices for master data services (ECS, Spring Boot / Python)

• Ensure delivery of scalable, resilient, and high-performing master data systems

• Drive observability, data quality monitoring, and lineage tracking

Master & Reference Data Implementation

• Implement end-to-end master data lifecycle flows (ingest → standardise → match → publish)

• Apply event-driven and API-based integration patterns for data synchronisation

• Ensure alignment to enterprise canonical data models and ACORD/industry standards where applicable

• Enforce config-driven / metadata-driven processing (e.g., YAML-based mappings and rules)

• Promote reusable ingestion, matching, and publishing patterns across domains

• Contribute to establishing the “golden path” for domain onboarding into MDM

• Work closely with governance tools (e.g., Collibra) to ensure metadata, lineage, and quality visibility

Data Governance, Quality & Standards

• Embed data quality rules, validation frameworks, and exception handling into pipelines

• Define and enforce data stewardship workflows and ownership models

• Support implementation of reference data management (hierarchies, code sets, taxonomies)

• Ensure auditability, traceability, and compliance across master data processes

What we’re looking for?

We are searching for an experienced engineer (5+ years) with a proven expertise in:

• Master Data Management (MDM) and/or Reference Data platforms (e.g., Reltio or any modern MDM platforms)

• Kafka / event-driven architectures (MSK preferred)

• AWS data stack (Glue, S3, ECS, Aurora/Postgres)

• Java (Spring Boot) and/or Python

• Strong understanding of:

  • Master Data Management concepts (golden record, survivorship, hierarchy management)

  • Data modelling (canonical models, 3NF, domain-driven design)

  • Data quality, data governance, and stewardship models

  • Integration patterns (API, event streaming, CDC)

• Hands-on experience leading engineers and driving platform delivery

• Experience building scalable, domain-oriented data platforms

• Ability to define reusable frameworks and “factory model” onboarding patterns

• Strong problem-solving, communication, and stakeholder management skills

• Experience working in large-scale transformation programmes

Nice to Have:

• Experience with insurance data domains (Customer, Policy, Risk, Product, Supplier)

• Knowledge of ACORD data standards

• Experience with metadata/catalogue tools (e.g., Collibra) and data modelling tools (e.g., Erwin)

• Familiarity with config-driven architectures and schema-driven processing (Avro/Schema Registry)