17 of 17 Amazon Redshift Jobs in the City of London

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, DBT, Airbyte More ❯

development. Translate business requirements into scalable technical solutions that support critical client outcomes. Analytics Engineering Leaders: Architect and optimise data pipelines, ELT workflows, and cloud warehouse platforms (Snowflake, BigQuery, Redshift). Lead teams working with dbt, SQL, Python, and Airflow to drive data transformation at scale. Ensure data governance, quality, and modelling standards are upheld across solutions. Work closely More ❯

What We're Looking For Hands-on Palantir Foundry expertise or transferable, client facing data engineering experience with large-scale data platforms such as: Snowflake, Databricks, AWS Glue/Redshift, Google BigQuery Software engineering skills in Python, Java, or TypeScript/React. Strong data modelling, pipeline development, and API design experience. Excellent problem-solving and communication skills. Why This More ❯

Collaborative, curious, and happy to work in a self-organising, fast-paced environment. Nice-to-Have Skills : Experience with GCP/BigQuery or other cloud data warehouses (e.g., Snowflake, Redshift). Familiarity with data orchestration tools (e.g., Airflow). Experience with data visualisation platforms (such as Preset.io/Apache Superset or other). Exposure to CI/CD pipelines More ❯

Collaborative, curious, and happy to work in a self-organising, fast-paced environment. Nice-to-Have Skills : ·Experience with GCP/BigQuery or other cloud data warehouses (e.g., Snowflake, Redshift). ·Familiarity with data orchestration tools (e.g., Airflow). ·Experience with data visualisation platforms (such as Preset.io/Apache Superset or other). ·Exposure to CI/CD pipelines More ❯

Collaborative, curious, and happy to work in a self-organising, fast-paced environment. Nice-to-Have Skills : Experience with GCP/BigQuery or other cloud data warehouses (e.g., Snowflake, Redshift). Familiarity with data orchestration tools (e.g., Airflow). Experience with data visualisation platforms (such as Preset.io/Apache Superset or other). Exposure to CI/CD pipelines More ❯

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, but not More ❯

doing: Designing 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 More ❯

innovation. Architect and optimise ELT pipelines and workflows using dbt, Airflow, SQL, and Python. Oversee the design and evolution of large-scale analytics platforms and data warehouses (Snowflake, BigQuery, Redshift). Ensure best practices in governance, modelling, data quality, and scalable system design. Drive continuous improvement in orchestration, schema design, transformation, and automation processes. Translate complex technical insights into More ❯

Skillset Delivery experience Building solutions in snowflake implementing data warehousing solutions using Snowflake and AWS Hands-on experience with AWS services such as Glue (Spark), Lambda, Step Functions, ECS, Redshift, and SageMaker. Enthusiasm for cross-functional work and adaptability beyond traditional data engineering. Examples like building APIs, integrating with microservices, or contributing to backend systems - not just data pipelines … 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 components and APIs … preferred for data processing, automation, and pipeline development AWS or Snowflake certifications are a plus Hands-on experience with AWS services such as Glue (Spark), Lambda, Step Functions, ECS, Redshift, and SageMaker. Enthusiasm for cross-functional work and adaptability beyond traditional data engineering. Examples like building APIs, integrating with microservices, or contributing to backend systems - not just data pipelines More ❯

will play a pivotal role in designing and implementing data warehousing solutions using Snowflake and AWS. You will help drive the evolution of data architecture as we move from Redshift to Snowflake. Hands-on experience with AWS services such as Glue (Spark), Lambda, Step Functions, ECS, Redshift, and SageMaker. Conducting code reviews, mentoring through pair programming. Building APIs … 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 development More ❯

will play a pivotal role in designing and implementing data warehousing solutions using Snowflake and AWS. You will help drive the evolution of data architecture as we move from Redshift to Snowflake. Hands-on experience with AWS services such as Glue (Spark), Lambda, Step Functions, ECS, Redshift, and SageMaker. Conducting code reviews, mentoring through pair programming. Building APIs … 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 development More ❯

storage, and data governance across AWS and Snowflake platforms. Lead the design of data lakes, data warehouses, and real-time data pipelines using AWS-native tools such as Glue, Redshift, S3, Lambda, Kinesis, and Athena. Project Leadership & Delivery Manage and deliver complex data architecture projects, providing technical leadership to cross-functional teams. Collaborate with business stakeholders, data engineers, and … years of experience in data architecture, including at least 3 years in a cloud-native environment. Proven expertise with AWS cloud services , including but not limited to: S3, Glue, Redshift, Lambda, Kinesis, Athena, Lake Formation. Strong hands-on experience with Snowflake – architecture, performance tuning, security, and data sharing features. Proficient in data modelling, data integration (ETL/ELT), and More ❯

with a focus on data projects You have experience of establishing data analytics and supporting AI solutions You have a good working experience of AWS (e.g. S3, Kinesis, Glue, Redshift, Lambda and EMR) and/or Azure data services (e.g. ADF, Synapse, Fabric, Azure Functions) You have advanced client and stakeholder management skills What's in it for you More ❯

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 deployment … 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 collaboration skills For More ❯

model deployment and production monitoring. • Working knowledge of core data science concepts, such as model evaluation metrics, overfitting, data drift, and feature importance. • Proficiency in AWS services (like S3, RedShift etc) • Experience with Grafana for monitoring and alerting. • Good to have hands-on experience with Domino Data Lab platform. • Solid understanding of CI/CD pipelines, version control, containerization More ❯

model deployment and production monitoring. • Working knowledge of core data science concepts, such as model evaluation metrics, overfitting, data drift, and feature importance. • Proficiency in AWS services (like S3, RedShift etc) • Experience with Grafana for monitoring and alerting. • Good to have hands-on experience with Domino Data Lab platform. • Solid understanding of CI/CD pipelines, version control, containerization More ❯