1 to 25 of 326 Remote Amazon Redshift Jobs

teams to understand business objectives, identify data requirements, and define analytics goals. Develop and implement data analysis strategies using AWS analytics services, such as Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon QuickSight. Design and build robust data pipelines and ETL processes to extract, transform … techniques to perform predictive and prescriptive analyses, clustering, segmentation, and pattern recognition. Identify key metrics, develop meaningful KPIs, and build dashboards and visualisations using Amazon QuickSight to enable data-driven decision-making. Conduct exploratory data analysis to uncover trends, patterns, and insights that inform product enhancements, user behaviour, and … 2.Proven experience as a Data Scientist, preferably in a cloud-based environment using AWS analytics services. 3.Strong proficiency in AWS analytics services, such as Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon QuickSight. 4.Solid understanding of data modelling, ETL processes, and data warehousing concepts. 5.Proficiency More ❯

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 … 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 More ❯

SQLAlchemy, GitLab, Athena. Your Trusted Companions : Docker, Snowflake, MongoDB, Relational Databases (eg MySQL, PostgreSQL), Dagster, Airflow/Luigi, Spark, Kubernetes. Your AWS Kingdom : Lambda, Redshift, EC2, ELB, IAM, RDS, Route53, S3-the building blocks of cloud mastery. Your Philosophy : Continuous integration/deployments, (CI/CD) automation, rigorous code More ❯

and maintain robust data pipelines using AWS services (Glue, Lambda, Step Functions, S3, etc.) Develop and optimize data lake and data warehouse solutions using Redshift, Athena, and related technologies Collaborate with data scientists, analysts, and business stakeholders to understand data requirements Ensure data quality, governance, and compliance with financial More ❯

in some of the following technologies, Databricks, SQL, Python, and Spark for large-scale data processing. Strong expertise in data warehousing technologies (e.g., Snowflake, Redshift, BigQuery, or similar). Experience with distributed data processing frameworks (Spark, Hadoop, or similar). Deep understanding of ETL processes, data modeling, and schema More ❯

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 More ❯

grade data solutions, fostering a culture of innovation, collaboration, and continuous learning. Key Responsibilities: Design, develop, test, and deploy data integration pipelines in AWS (Redshift, Glue, Athena, Lambda, S3). Lead and mentor a team of Data Engineers, guiding their technical work and professional growth. Create technical documentation such More ❯

platforms such as Microsoft Azure, AWS, or Google Cloud Platform. In-depth knowledge of cloud-native data services (e.g., Databricks, Snowflake, Microsoft Fabric, AWS Redshift). Strong understanding of data modelling (relational, dimensional, NoSQL) and ETL/ELT processes . Experience with data integration tools (e.g., Apache Kafka, Talend More ❯

recommend improvements for data ownership and stewardship. Qualifications Programming: Python, SQL, Scala, Java. Big Data: Apache Spark, Hadoop, Databricks, Snowflake, etc. Cloud: AWS (Glue, Redshift), Azure (Synapse, Data Factory, Fabric), GCP (BigQuery, Dataflow). Data Modelling & Storage: Relational (PostgreSQL, SQL Server), NoSQL (MongoDB, Cassandra), Dimensional modelling. DevOps & Automation: Docker More ❯

dbt or Google Cloud Platform or related technologies. • Experience with other cloud platforms (e.g. AWS, Azure, Snowflake) and data warehouse/lakehouse technologies (e.g. Redshift, Databricks, Synapse) • Knowledge of distributed big data technologies. • Proficiency in Python. • Familiarity with data governance and compliance frameworks. Your characteristics as a Consultant will More ❯

presenting at webinars, and engaging in external speaking opportunities. Technical Delivery Excellence You’ll design and optimise cloud-based data architectures (e.g. Snowflake, Databricks, Redshift, Google BigQuery, Azure Synapse) to support advanced analytics. You’ll build and automate scalable, secure, and high-performance data pipelines handling diverse data sources. More ❯

Implement best practices for data security and compliance. Collaborate with stakeholders and external partners. Skills & Experience: Strong experience with AWS data technologies (e.g., S3, Redshift, Lambda). Proficient in Python, Apache Spark, and SQL. Experience in data warehouse design and data migration projects. Cloud data platform development and deployment. More ❯

preferred) Experience with cloud platforms (AWS/Azure) Solid understanding of data architecture, modelling, and ETL/ELT pipelines Experience using tools like Databricks, Redshift, Snowflake, or similar Comfortable working with APIs, CLIs, and orchestration tools like Airflow Confident using Git and familiar with CI/CD processes (Azure More ❯

Implement best practices for data security and compliance. Collaborate with stakeholders and external partners. Skills & Experience: Strong experience with AWS data technologies (e.g., S3, Redshift, Lambda). Proficient in Python, Apache Spark, and SQL. Experience in data warehouse design and data migration projects. Cloud data platform development and deployment. More ❯

recommend improvements for data ownership and stewardship. Qualifications Programming: Python, SQL, Scala, Java. Big Data: Apache Spark, Hadoop, Databricks, Snowflake, etc. Cloud: AWS (Glue, Redshift), Azure (Synapse, Data Factory, Fabric), GCP (BigQuery, Dataflow). Data Modelling & Storage: Relational (PostgreSQL, SQL Server), NoSQL (MongoDB, Cassandra), Dimensional modelling. DevOps & Automation: Docker More ❯

on a data warehouse solution irrespective of underlying technology. Experience using cloud data warehouse technology would also be beneficial - Snowflake (preferred), Google BigQuery, AWS Redshift or Azure Synapse. Data Pipeline(highly desirable): Demonstrable experience working with data from a wide variety of data sources including different database platforms, flat More ❯

presenting at webinars, and engaging in external speaking opportunities. Technical Delivery Excellence You'll design and optimise cloud-based data architectures (e.g. Snowflake, Databricks, Redshift, Google BigQuery, Azure Synapse) to support advanced analytics. You'll build and automate scalable, secure, and high-performance data pipelines handling diverse data sources. More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯

broad exposure, technical ownership, and the opportunity to influence firm-wide data architecture. Key Responsibilities Build scalable data pipelines using AWS (Glue, Lambda, EventBridge, Redshift) Ingest, normalise, and validate data from vendors (Bloomberg, Refinitiv, exchanges) Enhance platform capabilities for historical, real-time, and alternative datasets Monitor data quality and More ❯