Senior Data Platform Engineer

Job Title :
Senior Data Platform Engineer

Job Location :
Guilford, UKJob Type: Permanent

Job Description:

Overall Objectives of the Job We are seeking a skilled and experienced Senior Data / Platform Engineer to join our Data & Analytics team. This hybrid role combines hands-on data engineering on Databricks and Azure Synapse with platform administration responsibilities across our cloud data estate. The role holder will design, build, and operate scalable data pipelines while also maintaining the underlying Azure platform including infrastructure-as-code (Pulumi), CI/CD automation, monitoring, security, and Databricks workspace administration.

The ideal candidate combines strong Python/PySpark engineering skills with deep Azure platform knowledge and a service-excellence mindset, ensuring the Client Data Technology platform remains secure, reliable, and aligned to Client standards.

Duties and Responsibilities Lead solution design activities, collaborating with peers and mentoring junior colleagues to define and execute the team backlog.

Develop, test, and document scalable ETL/ELT data pipelines and workflows using Databricks and Azure Synapse to ingest and transform data from a variety of sources.

Administer and maintain Azure data platform components including Synapse, Databricks, ADLS Gen2, Key Vault, networking (VNets, NSGs, Managed Private Endpoints) and access control (RBAC, ACLs).Manage infrastructure-as-code across Dev, Staging, and Production environments using Pulumi (and equivalents such as Terraform / Bicep).Design and operate CI/CD pipelines using Git Hub Actions (with OIDC federation) and/or Azure Dev Ops, supporting trunk-based development practices.

Administer Databricks work spaces cluster policies, Secret Scopes, Repos/Git integration, Workflow job health, and Unity Catalog governance.

Monitor platform and pipeline health using Azure Monitor, Log Analytics, KQL, and Azure Dashboards; triage and resolve incidents.

Implement robust data security and ensure compliance with data privacy regulations; manage service principals, Managed Identities, and least-privilege access.

Carry out routine platform operations: patching, backups, storage lifecycle, tagging, access reviews, DR readiness, and runbook execution.

Identify and address performance bottlenecks and data quality issues to ensure data accuracy and reliability.

Work with testers to ensure automated test plans are in place and agree test packs for UAT; review peers' work and take accountability for the quality of squad deliverables.

Collaborate with stakeholders and analysts to understand data requirements and deliver clean, reliable, accessible data.

Ensure solution designs align with the Client Data Technology Strategy, maintaining and enhancing Client standards and Service Excellence.

Maintain technical documentation to Client standards (e.g., Grimlock) and stay current with industry trends, driving continuous improvement and innovation.

Qualification, Experience, Technical and Functional Skills Bachelor's or Master's degree in Computer Science, Information Systems, or a related field, with 610 years of relevant experience in data engineering and Azure platform administration.

Must Have Databricks: hands-on experience building and optimizing pipelines, managing Delta Lake, and administering work spaces (cluster policies, Unity Catalog, Secret Scopes, Workflows).Python / PySpark: strong programming skills for data processing, automation, and scripting.

Azure data stack:
Synapse, Databricks, ADLS Gen2, Key Vault including Linked Services, Managed Identity, and Spark Pool configuration.

Azure platform fundamentals: compute, storage, networking (VNets, NSGs, Private Endpoints), identity and RBAC.CI/CD:
Git Hub Actions (with OIDC federation) and/or Azure Dev Ops for data and platform deployments.

Infrastructure-as-Code:
Pulumi (or Terraform / Bicep) across multiple environments.

Scripting:
Power Shell and Bash for platform automation.

Monitoring & observability:
Azure Monitor, Log Analytics, KQL.Big data file formats:
Parquet and Delta Lake.

Cloud-native data modelling and ETL/ELT frameworks on Azure.

Good to Have Azure certifications: AZ-104, AZ-400, DP-203.Azure Data Factory (ADF), Microsoft Purview, Microsoft Fabric.

Data governance: lineage, cataloguing, sensitivity labels.

Event-driven architectures:
Kafka / Azure Event Hubs.

Delta Live Tables, MLflow.

IDMC Secure Agent, Power Automate flows.

Security baselines: CIS / NIST.Observability tooling:
Open Telemetry, Datadog.

AI tools and their application in data engineering.

Knowledge of the UK Insurance Market.

Agile / Scrum delivery experience.
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