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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Platform Engineer - Python & AWS - **Company:** Permutable - **Location:** London, UK (Remote available) - **Experience:** Expert - **Salary:** £47,924.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Automation of Tests, Big Data, Databases, Continuous Integration, Data Deduplication, Information Engineering, Data Infrastructure, Data Security, Linux, Distributed Systems, Github, Python (Programming Language), PostgreSQL, Natural Language Processing, Operational Databases, Standard Sql, Software Engineering, SQL Databases, Data Streaming, Data Logging, Pulumi, Data Ingestion, Delivery Pipeline, Large Language Models, Event Driven Architecture, Kubernetes, Infrastructure Automation Frameworks, Information Technology, AWS Data Analytics, Data Management, Machine Learning Operations, Hardware Infrastructure, Terraform, Stream Processing, Data Pipelines - **Published:** October 6, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5912353092 ## About the Role Strong production Python experience: You can design, build, test and maintain substantial Python services and data pipelines. Strong AWS experience: Hands-on experience building and operating production systems on AWS, including services such as S3, RDS/PostgreSQL, Lambda, ECS or EKS. Experience building data platforms: A substantial track record in data platform engineering, data engineering or backend engineering, with ownership of production data systems. SQL and database skills: Strong SQL and practical experience with PostgreSQL, data modelling, query performance and processing large datasets. Event-driven and distributed systems experience: A solid understanding of messaging, queues, asynchronous processing and reliable workflows, including retries and failure recovery. Infrastructure-as-Code and CI/CD: Experience with Pulumi, Terraform or equivalent, alongside automated testing and deployment pipelines. Production ownership: Comfortable working with Linux, containers, monitoring and logging, and diagnosing problems across application code, pipelines and infrastructure. Engineering judgement: Able to make practical architecture decisions and balance performance, reliability, cost and delivery speed. Startup mindset: Proactive, resourceful and comfortable taking responsibility in a small, fast-moving engineering team. Relevant technical background: A degree in computer science, software engineering or a related discipline, or equivalent practical experience. Useful additional experience: Apache Airflow, real-time data processing, Kubernetes, AI/ML infrastructure, LLM workflows, GPU infrastructure or financial-market data. ## Description We're looking for an experienced Data Platform Engineer with strong Python and AWS skills to build and scale the systems behind our data ingestion, processing, storage and delivery. You'll write production Python, develop reliable data pipelines and own the AWS infrastructure that runs them. As we move towards a more real-time, event-driven architecture, you'll help shape how data flows through our platform and reaches our models and clients. Working directly with our founder and engineering team, you'll have significant responsibility for architecture, implementation and production performance. Build production data pipelines: Design and develop Python services and pipelines to ingest, validate, transform and deliver large volumes of news, market and proprietary data. Develop our AWS data platform: Build and scale infrastructure across S3, RDS/PostgreSQL, Lambda, ECS and EKS, choosing the right services for each workload. Build event-driven systems: Develop services using messaging, queues and asynchronous processing to support faster data updates and reliable distributed workflows. Improve data quality and reliability: Build validation, deduplication, schema handling, retries and recovery into our pipelines. Make it straightforward to identify missing data, investigate failures and reprocess historical datasets. Own storage and data access: Improve how we store, query and serve data to support research, production models and client-facing applications. Support AI and quantitative workloads: Work with AI engineers and researchers to provide reliable data inputs and integrate NLP, LLM and quantitative model outputs into the platform. Automate infrastructure and deployments: Manage infrastructure through Pulumi and build CI/CD workflows using GitHub Actions. Monitor and optimise production: Develop monitoring and alerting for pipeline health, data freshness and service performance. Diagnose production issues and improve processing speed, resilience and AWS costs.