1 to 25 of 665 Permanent Amazon Redshift Jobs

Core Azure SQL and MSSQL server Deep understanding of Azure-native services for data engineering. Experience with Azure Key Vault, Azure Monitor, and RBAC. AWS: Experience with S3, Glue, Redshift, and Lambda. Familiarity with IAM, CloudWatch, and Athena. Experience with BigQuery, Cloud Storage, and Dataflow. Familiarity with Cloud Functions, IAM, and Vertex AI (optional). Experience in one or More ❯

and data pipeline design. Hands-on experience with data lakehouse, warehouse, and streaming data architectures. Working knowledge of SQL, Python, and relevant data engineering frameworks (e.g. Databricks, Synapse, Snowflake, Redshift). Experience designing data platforms leveraging PaaS and SaaS solutions. Solid understanding of information governance, metadata management, and master data management principles. Experience leading data design across full project More ❯

and data pipeline design. Hands-on experience with data lakehouse, warehouse, and streaming data architectures. Working knowledge of SQL, Python, and relevant data engineering frameworks (e.g. Databricks, Synapse, Snowflake, Redshift). Experience designing data platforms leveraging PaaS and SaaS solutions. Solid understanding of information governance, metadata management, and master data management principles. Experience leading data design across full project More ❯

ELT processes, automation Technical Requirements: - Strong proficiency in SQL and Python programming - Extensive experience with data modeling and data warehouse concepts - Advanced knowledge of AWS data services, including: S3, Redshift, AWS Glue, AWS Lambda - Experience with Infrastructure as Code using AWS CDK - Proficiency in ETL/ELT processes and best practices - Experience with data visualization tools (Quicksight) Required Skills More ❯

environments. Relevant Skills: Essential Skills: Proficiency in Python or Scala for data processing Strong SQL skills for querying and managing relational databases Experience with AWS services including S3, Glue, Redshift, Lambda, and Athena Knowledge of ETL processes and data pipeline development Understanding of data modelling and data warehousing concepts Familiarity with version control systems, particularly Git Desirable Skills: Experience More ❯

environments. Relevant Skills: Essential Skills: Proficiency in Python or Scala for data processing Strong SQL skills for querying and managing relational databases Experience with AWS services including S3, Glue, Redshift, Lambda, and Athena Knowledge of ETL processes and data pipeline development Understanding of data modelling and data warehousing concepts Familiarity with version control systems, particularly Git Desirable Skills: Experience 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 ❯

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 ❯

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 ❯

warehousing, data lakes, and ETL processes. Programming: Experience with programming languages like Python, Java, or Scala. Cloud Data Platform: Strong experience with Cloud Data Platform like Databricks (preferred) , Snowflake, Redshift, etc. Databricks Expertise: Deep understanding of Databricks architecture, including clusters, jobs, notebooks, and workspaces. Database Technologies: Knowledge of relational and NoSQL databases. Infrastructure as Code: Experience with tools like More ❯

engineers and support their growth. 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. Expertise across data warehouse and More ❯

AWS (preferred) or Azure , and experience managing data pipelines in the cloud. Strong skills in Python and SQL , with a solid grasp of data warehousing concepts. Experience with DBT , Redshift , or Postgres , and familiarity with BI tools (Power BI preferred). Proven understanding of CI/CD principles , DevOps, and infrastructure automation. Excellent communication skills — able to collaborate across More ❯

AWS (preferred) or Azure , and experience managing data pipelines in the cloud. Strong skills in Python and SQL , with a solid grasp of data warehousing concepts. Experience with DBT , Redshift , or Postgres , and familiarity with BI tools (Power BI preferred). Proven understanding of CI/CD principles , DevOps, and infrastructure automation. Excellent communication skills — able to collaborate across More ❯

AWS (preferred) or Azure , and experience managing data pipelines in the cloud. Strong skills in Python and SQL , with a solid grasp of data warehousing concepts. Experience with DBT , Redshift , or Postgres , and familiarity with BI tools (Power BI preferred). Proven understanding of CI/CD principles , DevOps, and infrastructure automation. Excellent communication skills - able to collaborate across More ❯

Requirements Proven experience as a Data Engineer, with a track record of designing and managing scalable data pipelines and infrastructure in AWS. Strong expertise with AWS services, including Glue, Redshift, Data Catalog, and large-scale data storage solutions such as data lakes. Proficiency in ETL/ELT tools (e.g. Apache Spark, Airflow, dbt). Skilled in data processing languages More ❯

and big data platforms. Knowledge of data modeling, replication, and query optimization. Hands-on experience with SQL and NoSQL databases is desirable. Familiarity with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) would be beneficial. Data Platform Management: Comfortable operating in hybrid environments (cloud and on-prem). Experience integrating diverse data sources and systems. Understanding of secure data transfer More ❯

and big data platforms. Knowledge of data modeling, replication, and query optimization. Hands-on experience with SQL and NoSQL databases is desirable. Familiarity with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) would be beneficial. Data Platform Management: Comfortable operating in hybrid environments (cloud and on-prem). Experience integrating diverse data sources and systems. Understanding of secure data transfer More ❯

Engineering with strong Python and SQL skills. Background in building and maintaining ETL/ELT pipelines in a cloud environment (AWS or Azure). Knowledge of data warehousing (e.g., Redshift, Postgres) and tools like DBT . Familiarity with CI/CD , data modelling, and Agile practices. Comfortable collaborating with Data Scientists and translating business needs into technical solutions. Experience More ❯

cloud technologies for ETL pipeline, data warehouse and data lake design/building and data movement. AWS data and analytics services (or open-source equivalent) such as EMR, Glue, RedShift, Kinesis, Lambda, DynamoDB. What you can expect Work to agile best practices and cross-functionally with multiple teams and stakeholders. You’ll be using your technical skills to problem More ❯

cloud technologies for ETL pipeline, data warehouse and data lake design/building and data movement. AWS data and analytics services (or open-source equivalent) such as EMR, Glue, RedShift, Kinesis, Lambda, DynamoDB. What you can expect Work to agile best practices and cross-functionally with multiple teams and stakeholders. You’ll be using your technical skills to problem More ❯

cloud technologies for ETL pipeline, data warehouse and data lake design/building and data movement. AWS data and analytics services (or open-source equivalent) such as EMR, Glue, RedShift, Kinesis, Lambda, DynamoDB. What you can expect Work to agile best practices and cross-functionally with multiple teams and stakeholders. You’ll be using your technical skills to problem More ❯

Familiarity with ETL tools and big data platforms. Knowledge of data modelling, replication, and query optimization. Experience with SQL and NoSQL databases. Exposure to data warehousing tools (e.g., Snowflake, Redshift, BigQuery). Data Platform Management Experience in hybrid environments (cloud/on-prem). Skilled in integrating diverse data sources. Understanding of secure data transfer and disaster recovery planning. More ❯

team of highly technically skilled engineers, who are enhancing Creditsafe’s data offerings with high throughput and scalability as primary goals. The data delivery platform is built upon AWS Redshift and S3 cloud storage. The platform manages billions of objects, with daily increments in the 10s of millions. Our ETL processes are built in Python and Rust, efficiently transforming More ❯

Core Skills Cloud Expertise: Azure, AWS, SAP Data Engineering: ELT/ETL, Data Modeling, Data Warehousing Tools & Tech: Azure Data Factory, Databricks, Synapse, Stream Analytics, AWS Glue, Airflow, Kinesis, Redshift, SQL, GitHub, Azure DevOps, SonarQube, PyTest Preferred Experience Leading agile or scrum-based technical teams. Working with financial or planning data. Using documentation tools (e.g., MKDocs ). Why You More ❯

Core Skills Cloud Expertise: Azure, AWS, SAP Data Engineering: ELT/ETL, Data Modeling, Data Warehousing Tools & Tech: Azure Data Factory, Databricks, Synapse, Stream Analytics, AWS Glue, Airflow, Kinesis, Redshift, SQL, GitHub, Azure DevOps, SonarQube, PyTest Preferred Experience Leading agile or scrum-based technical teams. Working with financial or planning data. Using documentation tools (e.g., MKDocs ). Why You More ❯