18 of 18 Remote Amazon S3 Jobs in Central London

data engineer with strong hands-on programming skills and software engineering fundamentals, with experience building scalable solutions in cloud environments (AWS preferred) Extensive experience in AWS services, e.g. EC2, S3, RDS, DynamoDB, Redshift, Lambda, API Gateway Solid foundation in software engineering principles, including version control (Git), testing, CI/CD, modular design, and clean code practices. Experience developing reusable More ❯

infrastructure strategy, drive automation, and strengthen CI/CD practices to keep our technology reliable and scalable. Required Technical Skills 5+ years AWS experience (VPC, EC2, ECS, EKS, EFS, S3, Workspaces, Security Groups, CloudWatch/CloudTrail, IAM). Strong containerization expertise (Docker or similar). Scripting and automation with Python or Bash. Modern CI/CD practices and pipeline More ❯

About You AWS certification (Solutions Architect/DevOps Engineer – Associate or Professional level) 5+ years’ experience in cloud architecture, design, and implementation Expertise with AWS services such as EC2, S3, RDS, Lambda, VPC, IAM, and CloudFormation or Terraform Strong experience with DevOps tools (Git, Jenkins, Docker, Kubernetes) Knowledge of cloud security and compliance frameworks (CIS, SOC 2, etc.) Proven More ❯

About You AWS certification (Solutions Architect/DevOps Engineer – Associate or Professional level) 5+ years’ experience in cloud architecture, design, and implementation Expertise with AWS services such as EC2, S3, RDS, Lambda, VPC, IAM, and CloudFormation or Terraform Strong experience with DevOps tools (Git, Jenkins, Docker, Kubernetes) Knowledge of cloud security and compliance frameworks (CIS, SOC 2, etc.) Proven More ❯

Tools like GitHub Actions, Jenkins, AWS CDK, CloudFormation, Terraform. Key Responsibilities: Design and implement scalable, secure, and cost-efficient data solutions on AWS, leveraging services such as Glue, Lambda, S3, Redshift, and Step Functions. Lead the development of robust data pipelines and analytics platforms, ensuring high availability, performance, and maintainability. Demonstrate proficiency in software engineering principles, contributing to the More ❯

Tools like GitHub Actions, Jenkins, AWS CDK, CloudFormation, Terraform. Key Responsibilities: Design and implement scalable, secure, and cost-efficient data solutions on AWS, leveraging services such as Glue, Lambda, S3, Redshift, and Step Functions. Lead the development of robust data pipelines and analytics platforms, ensuring high availability, performance, and maintainability. Demonstrate proficiency in software engineering principles, contributing to the More ❯

design AI-powered agent solutions that integrate with enterprise systems and cloud platforms. Develop and optimize RESTful and GraphQL APIs to facilitate AI-driven interactions. Utilize AWS services (Lambda, S3, API Gateway, SageMaker, DynamoDB, ECS, etc.) to deploy scalable AI solutions. Implement Full Stack JavaScript (Node.js, React.js, Express, TypeScript, Next.js, etc.) applications to support AI-driven interfaces. Work closely More ❯

hands-on technical involvement in key deals. Technical Expertise Cloud-Native Architectures: - Deep understanding of AWS, Azure, and GCP services, including compute (EC2, Lambda), networking (VPC, Transit Gateway), storage (S3, Blob), and container orchestration (ECS, Fargate, Kubernetes/EKS/GKE). - Familiarity with IaC tools such as Terraform, CloudFormation, and Helm for scalable deployments. AI/ML Fundamentals More ❯

security and compliance. Build and maintain CI/CD pipelines, monitoring tools, and cost optimisation strategies. Mentor and coach engineers on platform best practices. Tech Stack Highlights AWS (ECS, S3, DynamoDB, Aurora, OpenSearch) Pulumi (TypeScript) for infrastructure as code Kafka (Confluent Cloud) for event-driven architecture GitHub Actions for CI/CD DataDog for observability Containerised microservices architecture What More ❯

security and compliance. Build and maintain CI/CD pipelines, monitoring tools, and cost optimisation strategies. Mentor and coach engineers on platform best practices. Tech Stack Highlights AWS (ECS, S3, DynamoDB, Aurora, OpenSearch) Pulumi (TypeScript) for infrastructure as code Kafka (Confluent Cloud) for event-driven architecture GitHub Actions for CI/CD DataDog for observability Containerised microservices architecture What More ❯

collaborative development workflows. Good written and verbal communication; able to contribute to cross-functional discussions. Proven problem-solving and time management skills. Desirable: Familiarity with cloud services (e.g. AWS S3, Athena, Lambda). Basic understanding of data engineering or ML pipelines. Knowledge of distributed systems (e.g. Kafka). Interest in American sports or the betting industry. Some exposure to More ❯

scale Data Engineering projects Strong experience in Python/PySpark, Databricks & Apache Spark Hands on experience with both batch & streaming pipelines Strong experience in AWS and associated tooling (Eg, S3, Glue, Redshift, Lambda, Terraform etc) Experience designing Data Engineering platforms from scratch Alongside their commitment to work-life balance the business also has an extremely competitive benefits package including More ❯

What You’ll Bring Proven experience in data engineering, with a strong track record building AWS cloud data pipelines and data warehouses. Solid understanding of AWS services such as S3 and Redshift, particularly around storage, computation and security. Familiarity with modern BI tools such as Power BI or AWS QuickSight. Experience with open-source data stack tools like Airflow More ❯

What You’ll Bring Proven experience in data engineering, with a strong track record building AWS cloud data pipelines and data warehouses. Solid understanding of AWS services such as S3 and Redshift, particularly around storage, computation and security. Familiarity with modern BI tools such as Power BI or AWS QuickSight. Experience with open-source data stack tools like Airflow More ❯

and building dashboards & automated reporting (Tableau, Power BI). Developing data pipelines, models and analyses using SQL & Python (Pandas/NumPy). Working with cloud environments (AWS Redshift/S3 or similar). Advising business stakeholders on how to use insights to drive measurable impact. Collaborating with experienced data consultants and supporting delivery across multiple industries. What we’re More ❯

and building dashboards & automated reporting (Tableau, Power BI). Developing data pipelines, models and analyses using SQL & Python (Pandas/NumPy). Working with cloud environments (AWS Redshift/S3 or similar). Advising business stakeholders on how to use insights to drive measurable impact. Collaborating with experienced data consultants and supporting delivery across multiple industries. What we’re More ❯

modernisation journey to Snowflake-based data warehousing solutions within an AWS environment. Design and implement scalable, secure, and cost-efficient data solutions using AWS services such as Glue, Lambda, S3, Redshift, and Step Functions. Develop robust data pipelines and analytics platforms, ensuring high availability, performance, and maintainability. Drive automation and CI/CD practices for data workflows, ensuring reliable … mentoring teams Proven experience as a data engineer with a strong focus on Snowflake and AWS services in large-scale enterprise environments Extensive experience with AWS services (e.g., EC2, S3, RDS, DynamoDB, Redshift, Lambda, API Gateway) Strong SQL skills for complex data queries and transformations Python programming for data processing and analysis is a plus Strong leadership, communication, and More ❯

next-generation data platform, with responsibilities including: Designing and implementing scalable data pipelines using Python and Apache Spark Building and orchestrating workflows using AWS services such as Glue , Lambda , S3 , and EMR Serverless Applying best practices in software engineering: CI/CD , version control , automated testing , and modular design Supporting the development of a lakehouse architecture using Apache Iceberg … understanding of data engineering fundamentals: ETL/ELT, schema evolution, batch processing Experience or strong interest in Apache Spark for distributed data processing Familiarity with AWS data tools (e.g., S3, Glue, Lambda, EMR) Strong communication skills and a collaborative mindset Comfortable working in Agile environments and engaging with stakeholders Bonus Skills Experience with Apache Iceberg or similar table formats More ❯