1 to 25 of 306 Amazon Redshift Jobs in the UK

stack with robust, pragmatic solutions. Responsibilities develop, and maintain ETL/ELT data pipelines using AWS services Data, Databricks and dbt Manage and optimize data storage solutions such as Amazon S3, Redshift, RDS, and DynamoDB. Implement and manage infrastructure-as-code (IaC) using tools like Terraform or AWS CloudFormation. Monitor and optimize the performance, cost, and scalability of More ❯

with data privacy regulations. Technical Competencies The role is a hands-on technical leadership role with advanced experience in at least most of the following technologies Cloud Platforms: AWS (Amazon Web Services): Knowledge of services like S3, EC2, Lambda, RDS, Redshift, EMR, SageMaker, Glue, and Kinesis. Azure: Proficiency in services like Azure Blob Storage, Azure Data Lake, VMs … Formation, Azure Purview. Data Security Tools: AWS Key Management Service (KMS), Azure Key Vault. Data Analytics & BI: Visualization Tools: Tableau, Power BI, Looker, and Grafana. Analytics Services: AWS Athena, Amazon QuickSight, Azure Stream Analytics. Development & Collaboration Tools: Version Control: Git (and platforms like GitHub, GitLab). CI/CD Tools: Jenkins, Travis CI, AWS CodePipeline, Azure DevOps. Other Key More ❯

from various sources including on-premise and cloud platforms. Develop and optimize data pipelines and data integration processes between on-premises systems and AWS cloud services such as S3, Redshift, RDS, Glue, and Lambda. Collaborate with Data Architects, Data Analysts, and BI developers to understand data requirements and deliver solutions. Ensure data quality, consistency, and governance across platforms. Implement …/star schema), and data integration patterns. Hands-on experience with SQL and relational databases (e.g., Oracle, SQL Server, PostgreSQL). Familiarity with AWS data services such as S3, Redshift, Glue, EMR, Lambda, or RDS. Knowledge of scripting languages (e.g., Python, Shell) for automation and orchestration. Experience working in Agile environments and using tools such as JIRA, Git, or More ❯

tenant, data-heavy systems, ideally in a startup or fast-moving environment. Technical Stack : Languages/Tools: Python (REST API integrations), DBT, Airbyte, GitHub Actions Modern Data Warehousing: Snowflake, Redshift, Databricks, or BigQuery. Cloud & Infra: AWS (ECS, S3, Step Functions), Docker (Kubernetes or Fargate a bonus) Data Modelling : Strong grasp of transforming structured/unstructured data into usable models More ❯

processing) Apache Spark Streaming, Kafka or similar (for real-time data streaming) Experience using data tools in at least one cloud service - AWS, Azure or GCP (e.g. S3, EMR, Redshift, Glue, Azure Data Factory, Databricks, BigQuery, Dataflow, Dataproc) Would you like to join us as we work hard, have fun and make history? Apply for this job indicates a More ❯

to design innovative data solutions that address complex business requirements and drive decision-making. Your skills and experience Proficiency with AWS Tools: Demonstrable experience using AWS Glue, AWS Lambda, Amazon Kinesis, Amazon EMR , Amazon Athena, Amazon DynamoDB, Amazon Cloudwatch, Amazon SNS and AWS Step Functions. Programming Skills: Strong experience with modern programming languages such … as Python, Java, and Scala. Expertise in Data Storage Technologies: In-depth knowledge of Data Warehouse, Database technologies, and Big Data Eco-system technologies such as AWS Redshift, AWS RDS, and Hadoop. Experience with AWS Data Lakes: Proven experience working with AWS data lakes on AWS S3 to store and process both structured and unstructured data sets. To be More ❯

maintain scalable data pipelines to ingest, process, and store large sets of financial data from various internal and external sources. Cloud Infrastructure Management : Leverage AWS services such as S3, Redshift, Lambda, Glue, and others to develop and maintain robust cloud-based data infrastructure. Automation : Use Terraform to automate infrastructure provisioning, deployment, and configuration, ensuring efficiency and repeatability in cloud … environments. Database Design & Optimisation : Design and optimise complex SQL queries, and relational databases (e.g., Amazon Redshift, PostgreSQL, MySQL) to enable fast, efficient data retrieval and analytics. Data Transformation : Apply ETL/ELT processes to transform raw financial data into usable insights for business intelligence, reporting, and predictive analytics. Collaboration with Teams : Work closely with platform team, data analysts … financial services or similar regulated industries. Strong understanding of data engineering concepts, including data modelling, ETL/ELT processes, and data warehousing. Proven experience with AWS services (e.g., S3, Redshift, Lambda, ECS, ECR, SNS, Eventbridge, CloudWatch, Athena etc.) for building and maintaining scalable data solutions in the cloud. Technical Skills (must have): Python: Proficient in Python for developing custom More ❯

structured and unstructured data using Python, R, SQL, AWS services and other tools. Build and maintain data repositories using AWS S3 and FSx technologies. Establish data warehousing solutions using Amazon Redshift. Build and maintain standard data models. Own data quality frameworks, validation processes and KPIs to ensure accuracy and consistency of data pipelines. Implement data versioning and lineage tracking … R&D. Required Knowledge, Skills and Abilities Expert knowledge of data engineering tools such as Python, R and SQL for data processing. Expert proficiency with AWS services particularly S3, Redshift, FSx, Glue, Lambda. Expert proficiency with relational databases. Strong background in data modeling and database design. Strong knowledge with unstructured database technologies (e.g. NoSQL) and other database types (e.g. More ❯

structured and unstructured data using Python, R, SQL, AWS services and other tools. Build and maintain data repositories using AWS S3 and FSx technologies. Establish data warehousing solutions using Amazon Redshift. Build and maintain standard data models. Own data quality frameworks, validation processes and KPIs to ensure accuracy and consistency of data pipelines. Implement data versioning and lineage tracking … R&D. Required Knowledge, Skills and Abilities Expert knowledge of data engineering tools such as Python, R and SQL for data processing. Expert proficiency with AWS services particularly S3, Redshift, FSx, Glue, Lambda. Expert proficiency with relational databases. Strong background in data modeling and database design. Strong knowledge with unstructured database technologies (e.g. NoSQL) and other database types (e.g. More ❯

structured and unstructured data using Python, R, SQL, AWS services and other tools. Build and maintain data repositories using AWS S3 and FSx technologies. Establish data warehousing solutions using Amazon Redshift. Build and maintain standard data models. Own data quality frameworks, validation processes and KPIs to ensure accuracy and consistency of data pipelines. Implement data versioning and lineage tracking … R&D. Required Knowledge, Skills and Abilities Expert knowledge of data engineering tools such as Python, R and SQL for data processing. Expert proficiency with AWS services particularly S3, Redshift, FSx, Glue, Lambda. Expert proficiency with relational databases. Strong background in data modeling and database design. Strong knowledge with unstructured database technologies (e.g. NoSQL) and other database types (e.g. More ❯

knowledge of SQL, Python, and data pipeline tools (e.g., Apache Airflow, dbt). Experience with cloud services (AWS, Azure, or GCP). Familiarity with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery). Excellent problem-solving skills and a collaborative mindset. Nice to Have Experience in consulting or working with multiple clients. Knowledge of DevOps practices and CI/CD More ❯

in a Principal or Lead role. Proven experience designing and delivering enterprise data strategies . Exceptional communication and stakeholder management skills. Expertise in enterprise-grade data warehouses (Snowflake, BigQuery, Redshift). Hands-on experience with Apache Airflow (or similar orchestration tools). Strong proficiency in Python and SQL for pipeline development. Deep understanding of data architecture, dimensional modelling, and More ❯

processing) Apache Spark Streaming, Kafka or similar (for real-time data streaming) Experience using data tools in at least one cloud service - AWS, Azure or GCP (e.g. S3, EMR, Redshift, Glue, Azure Data Factory, Databricks, BigQuery, Dataflow, Dataproc Would you like to join us as we work hard, have fun and make history? Apply for this job indicates a More ❯

Snr Data Engineer - Global Accounts, Professional Services, AWSI-SDT-APJ-Japan Job ID: Amazon Web Services Japan GK Our AWS Professional Services consultants deliver IT infrastructure and application architecture guidance, lead proof-of-concept projects, perform enterprise portfolio assessments, review operational best practices and conduct skills transfer workshops. AWS consultants collaborate with customers and partners to address security and … first organization. Key job responsibilities Expertise - Collaborate with AWS field sales, pre-sales, training and support teams to help partners and customers learn and use AWS services such as Amazon Elastic Compute Cloud (EC2), S3, AWS Glue, DynamoDB NoSQL, Relational Database Service (RDS), Elastic Map Reduce (EMR) and Amazon Redshift. Solutions - Deliver on-site technical engagements with partners … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most More ❯

and customise them for different use cases. Develop data models and Data Lake designs around stated use cases to capture KPIs and data transformations. Identify relevant AWS services - on Amazon EMR, Redshift, Athena, Glue, Lambda, to design an architecture that can support client workloads/use-cases; evaluate pros/cons among the identified options to arrive at More ❯

Skillset Delivery experience Building solutions in snowflake Insurance experience - advantageous but not necessary MUST HAVE SNOWFLAKE, AWS, SNOWPRO CORE Key Responsibilities: Lead the design and implementation of Snowflake [and Redshift] based data warehousing solutions within an AWS environment Mentoring of team members through code reviews and pair programming Build and support new AWS native cloud data warehouse solutions Develop … experience as a data engineer with a strong focus on Snowflake and AWS services in large-scale enterprise environments Extensive experience in 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 acumen for application health through performance More ❯

of domains — from predictive analytics to real-time personalization. ? What You'll Do: Architect and build scalable, secure, and maintainable data pipelines on AWS (Glue, Lambda, Step Functions, S3, Redshift, etc.) Operationalize machine learning models in production environments using SageMaker or custom deployment frameworks Automate and optimize ETL/ELT processes for structured and unstructured data Integrate CI/… business needs ? What We're Looking For: 5+ years of data engineering experience, with 2+ years in a senior role Deep expertise with AWS services (S3, Glue, Lambda, SageMaker, Redshift, etc.) Strong Python and SQL skills; experience with PySpark a bonus Familiarity with containerization (Docker), orchestration (Airflow, Step Functions), and infrastructure as code (Terraform/CDK) Solid understanding of More ❯

in a Principal or Lead role. Proven experience designing and delivering enterprise data strategies . Exceptional communication and stakeholder management skills. Expertise in enterprise-grade data warehouses (Snowflake, BigQuery, Redshift). Hands-on experience with Apache Airflow (or similar orchestration tools). Strong proficiency in Python and SQL for pipeline development. Deep understanding of data architecture, dimensional modelling, and More ❯

in a Principal or Lead role. Proven experience designing and delivering enterprise data strategies . Exceptional communication and stakeholder management skills. Expertise in enterprise-grade data warehouses (Snowflake, BigQuery, Redshift). Hands-on experience with Apache Airflow (or similar orchestration tools). Strong proficiency in Python and SQL for pipeline development. Deep understanding of data architecture, dimensional modelling, and More ❯

to minimise disruption. Collaborate with various teams to align migration processes with organisational goals and regulatory standards. Proficiency in AWS ETL technologies, including Glue, Data Sync, DMS, Step Functions, Redshift, DynamoDB, Athena, Lambda, RDS, EC2 and S3 Datalake, CloudWatch for building and optimizing ETL pipelines and data migration workflows. Working knowledge of Azure data engineering tools, including ADF (Azure More ❯

interested in building data and science solutions to drive strategic direction? Based in Tokyo, the Science and Data Technologies team designs, builds, operates, and scales the data infrastructure powering Amazon's retail business in Japan. Working with a diverse, global team serving customers and partners worldwide, you can make a significant impact while continuously learning and experimenting with cutting … working with large-scale data, excels in highly complex technical environments, and above all, has a passion for data. You will lead the development of data solutions to optimize Amazon's retail operations in Japan, turning business needs into robust data pipelines and architecture. Leveraging your deep experience in data infrastructure and passion for enabling data-driven business impact … with scientists, software engineers and business teams to identify and implement strategic data opportunities. Key job responsibilities Your key responsibilities include: - Create data solutions with AWS services such as Redshift, S3, EMR, Lambda, SageMaker, CloudWatch etc. - Implement robust data solutions and scalable data architectures. - Develop and improve the operational excellence, data quality, monitoring and data governance. BASIC QUALIFICATIONS - Bachelor More ❯

to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - continues to grow and evolve as a world-class e-commerce platform. Amazon's evolution from Web site to e-commerce partner to development platform is driven by the spirit of innovation that is part of the company's DNA. The world … minds come to to research and develop technology that improves the lives of shoppers and sellers around the world. About Team The RBS team is an integral part of Amazon online product lifecycle and buying operations. The team is designed to ensure Amazon remains competitive in the online retail space with the best price, wide selection and good … product information. The team's primary role is to create and enhance retail selection on the worldwide Amazon online catalog. The tasks handled by this group have a direct impact on customer buying decisions and online user experience. Overview of the role: A candidate will be a self-starter who is passionate about discovering and solving complicated problems, learning More ❯

and SQL Solid AWS experience (Glue, Lambda, SQS, S3, etc.) Experience designing/building/maintaining ETL pipelines Data modelling experience to forecast and analyse Data warehousing knowledge (PostgreSQL, Redshift, Snowflake etc.) Self-starter mindset, you’ll get stuck in and find answers independently to bring back to the team A drive to want to own projects and put More ❯

journey of our data platform (AWS) Cloud Proficiency: Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and its core data services (e.g., S3, Redshift, Lambda/Functions, Glue). Data Modelling: Deep understanding of ELT/ETL patterns, and data modelling techniques. CRM/Customer Data Focus: Experience working directly with data from More ❯

data scientists, and business stakeholders. Familiarity with cloud-based data ecosystems such as AWS, Azure, or GCP, and working with data warehouse/lakehouse technologies such as Snowflake, BigQuery, Redshift, or Athena/Glue. Essential: Proficient in writing clean, efficient, and maintainable SQL and Python code, particularly for data transformation and analytics use cases. Strong understanding of data modeling More ❯