Looking For Senior Platform Engineer(Trust & Compliance (Data Platform

• Design and build infrastructure-as-code for cloud-native data platform capabilities, based on reusable, composable constructs.

• Build and maintain CI/CD pipelines and deployment automation that customer teams use to provision and operate their environments.

• Contribute to automated, policy-driven access provisioning for users, groups, service principals and automation identities across multiple platforms.

• Contribute to compliance monitoring and auditing collecting, correlating and reporting on the compliance and access posture across all deployments.

• Develop reusable Python libraries and shared components consumed across the platform.

• Build reference data pipelines that showcase platform capabilities and codify guidelines and best practices for our customers.

• Establish and evolve engineering standards build, test, branching, release and deployment best practices.

• Partner with security, identity and governance stakeholders to translate compliance requirements into automated, enforceable controls.

• Troubleshoot, monitor and continuously improve the reliability and observability of the capabilities we ship.

Required skills and experience

We are looking for an experienced senior engineer with 5+ years of experience who is strong across DevOps and Cloud, with a genuine understanding of data engineering principles.

Core engineering

• Python and TypeScript — used to build cloud infrastructure-as-code based on AWS CDK constructs.
• Infrastructure as Code with AWS CDK — constructs and reusable patterns.
• CI/CD with Jenkins, Git and Bash scripting.
• Dependency and artifact management such as Artifactory, Python packaging and dependency management.
• Testing discipline — unit testing and integration testing as a first-class practice.
• Build, release and deployment best practices, including branching strategies such as GitFlow.
• AI-powered development tools such as GitHub Copilot and Claude Code as part of a modern engineering workflow.

Cloud networking and security

• Cloud networking fundamentals — VPC design, subnets, routing, security groups, VPC endpoints/PrivateLink and connectivity patterns.
• Cloud security best practices — network isolation, least-privilege design and secure-by-default infrastructure.
• Encryption and key management — AWS KMS, secrets management and data protection at rest and in transit.

Cloud and data platforms

• AWS for data-focused workloads including EMR, MWAA/Airflow, S3, Glue, Athena, Lambda, SQS, IAM, CloudWatch, and exposure to SageMaker and Bedrock.
• Databricks including workspace configuration, jobs, notebooks and Unity Catalog.
• Nice to have: Snowflake and Microsoft Azure.

Data engineering understanding

• Good understanding of modern data engineering stacks — primarily AWS and Databricks, with open-source stacks a plus.
• Understanding of data modelling principles and data processing paradigms, including batch and streaming.
• Familiarity with modern Lakehouse technologies; we standardise on Apache Iceberg.
• Data pipeline orchestration with Apache Airflow.
• Working knowledge of Apache Spark and SQL — core technologies in our platform.

Trust and Compliance focus

• Data access management principles, Zero Trust, and industry compliance frameworks, standards and best practices.
• Identity and access management: OIDC, SAML, Entra ID (Azure AD) and related identity technologies.
• Permission management across platforms — critical experience including the Databricks permission model, AWS IAM and AWS Lake Formation.