1 to 25 of 504 Amazon Redshift Jobs

on experience with Big Data ecosystems Hadoop, Spark, Kafka, Hive, HBase, etc. Strong experience with Cloud platforms (AWS/Azure/GCP) and services like: AWS: S3, Glue, EMR, Redshift, Lambda, Kinesis Azure: Data Factory, Synapse, Databricks, ADLS GCP: BigQuery, Dataflow, Pub/Sub Experience with Data Warehouse/Data Lake/Lakehouse design and modeling (Kimball, OLAP, OLTP More ❯

Strong hands-on experience with at least one major cloud platform (AWS, Azure, or Google Cloud Expertise in cloud-native data services such as AWS Glue, Lambda, S3, EMR, Redshift, Lake Formation, Azure Synapse, Data Factory, Databricks, or BigQuery. Advanced knowledge of SQL, Python, Spark, PySpark, and distributed data frameworks. Proven background in building ELT/ETL systems and More ❯

and analysis. These technologies are crucial for managing vast datasets and performing complex data transformations and aggregations. Cloud Data Management: Implement and oversee cloud-specific data services including AWS Redshift, S3, Google BigQuery, and Google Cloud Storage. Leverage cloud architectures to improve data sharing and interoperability across different business units. Security and Compliance: Ensure all data practices comply with More ❯

experience in ETL development and data engineering. Hands-on experience with Snowflake including data modeling, performance tuning, and SQL scripting. Proficiency in AWS services such as S3, Lambda, Glue, Redshift, and CloudWatch. Strong programming skills in Python or Scala for data processing. Experience with orchestration tools like Apache Airflow or AWS Step Functions. Familiarity with version control systems (e.g. More ❯

strategic business decisions. Responsibilities for the AWS Data Engineer: Design, build and maintain scalable data pipelines and architectures within the AWS ecosystem Leverage services such as AWS Glue, Lambda, Redshift, EMR and S3 to support data ingestion, transformation and storage Work closely with data analysts, architects and business stakeholders to translate requirements into robust technical solutions Implement and optimise More ❯

anomaly detection, and GenAI-powered automation Support GenAI initiatives through data readiness, synthetic data generation, and prompt engineering. Mandatory Skills: Cloud Platforms:Deep experience with AWS (S3, Lambda, Glue, Redshift) and/or Azure (Data Lake, Synapse). Programming & Scripting:Proficiency in Python, SQL, PySpark etc. ETL/ELT & Streaming:Expertise in technologies like Apache Airflow, Glue, Kafka, Informatica More ❯

big data processing frameworks (e.g., Spark, Flink). Strong experience with cloud platforms (AWS, Azure, GCP) and related data services. Hands-on experience with data warehousing tools (e.g., Snowflake, Redshift, BigQuery), Databricks running on multiple cloud platforms (AWS, Azure and GCP) and data lake technologies (e.g., S3, ADLS, HDFS). Expertise in containerization and orchestration tools like Docker and More ❯

Data Engineers to join a large on prem to AWS migration programme in replicating data pipelines in readiness for a lift and shift to the cloud. Essential: AWS and Redshift In full: Role purpose Reporting to the Lead Data Engineer, the Data Engineer is responsible for designing and maintaining scalable data pipelines, ensuring data availability, quality, and performance to More ❯

AWS, or GCP) and cloud-native BI services. Strong understanding of data warehousing, OLAP, semantic models, and query optimization. Experience working with modern data platforms such as Snowflake, Databricks, Redshift, BigQuery, or Synapse. Soft Skills Excellent communication and collaboration skills. Ability to lead technical teams and manage multiple workstreams. Strong problem-solving, analytical thinking, and decision-making skills. Ability More ❯

based architecture. You will need to have skills and experience in the following: Cloud Data Architecture: Strong knowledge of modern, cloud-based data architecture and tooling (e.g., S3, Glue, Redshift, Athena, Lake Formation, Iceberg/Delta). AWS Platform Build: Demonstrable experience designing and building modern data platforms in AWS. ETL/Orchestration Expertise: Expertise in ETL/ELT More ❯

based architecture. You will need to have skills and experience in the following: Cloud Data Architecture: Strong knowledge of modern, cloud-based data architecture and tooling (e.g., S3, Glue, Redshift, Athena, Lake Formation, Iceberg/Delta). AWS Platform Build: Demonstrable experience designing and building modern data platforms in AWS. ETL/Orchestration Expertise: Expertise in ETL/ELT More ❯

with analysts and architects to ensure compliance with government security standards. Troubleshoot and resolve issues in complex cloud environments. Essential Skills Strong experience with AWS services (Glue, Lambda, S3, Redshift, IAM). Proficiency in Python and SQL for data engineering tasks. Knowledge of data modelling, ETL frameworks, and best practices. Familiarity with security and compliance in government or regulated More ❯

with analysts and architects to ensure compliance with government security standards. Troubleshoot and resolve issues in complex cloud environments. Essential Skills Strong experience with AWS services (Glue, Lambda, S3, Redshift, IAM). Proficiency in Python and SQL for data engineering tasks. Knowledge of data modelling, ETL frameworks, and best practices. Familiarity with security and compliance in government or regulated More ❯

or dbt. Strong SQL skills and experience with cloud platforms (Azure preferred). Solid programming background in Python, Scala, or Java. Knowledge of data warehousing solutions (e.g. Snowflake, BigQuery, Redshift). Strong understanding of data governance, security, and compliance (experience within financial services is a plus). Leadership experience, with the ability to mentor, influence, and set technical direction. More ❯

/healthcare dataset or similar experience, including PHI/PII handling. Familiarity with SAS and R workflows to support epidemiologists and analysts. Experience with additional SQL platforms (Postgres, Snowflake, Redshift, BigQuery). Familiarity with data quality frameworks (Great Expectations, Deequ). Experience with real-time/streaming tools (Kafka, Spark Streaming). Familiarity with big data frameworks for large 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 ❯

/healthcare dataset or similar experience, including PHI/PII handling. Familiarity with SAS and R workflows to support epidemiologists and analysts. Experience with additional SQL platforms (Postgres, Snowflake, Redshift, BigQuery). Familiarity with data quality frameworks (Great Expectations, Deequ). Experience with real-time/streaming tools (Kafka, Spark Streaming). Familiarity with big data frameworks for large More ❯

expertise in SQL, NoSQL and data integration technologies. Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud. Experience with data warehousing technologies such as Snowflake, Redshift, BigQuery, Synapse, or Teradata. Skilled in ETL/ELT tools and data pipeline technologies such as Informatica, Talend, Databricks, or Apache tools. Strong understanding of data governance, MDM, metadata More ❯

databases (SQL Server, Oracle, MySQL, NoSQL) to AWS cloud. Proven experience in on-prem to AWS data migration using AWS-native tools such as DMS, Glue, DataSync, Step Functions, Redshift, Athena, Lambda, RDS, EC2, S3 Data Lake, and CloudWatch. Strong knowledge of data extraction, transformation, and loading (ETL) processes, leveraging tools such as Talend, Informatica, Matillion, Pentaho, MuleSoft, Boomi More ❯

scale that to 500 million events per day. You'll be using Kafka for real-time data streaming and AWS to build, optimise and monitor data warehousing solutions in Amazon RedShift, owning and managing AWS based systems to ensure cost effective, secure and high performance data operations. Location/WFH: You can work from home from anywhere in … data engineering skills and experience, having been through multiple end-to-end data pipeline builds You have a deep knowledge of AWS cloud services including: S3, EC2, Lambda, RDS, RedShift, Glue as well as Kinesis, MongoDB and PostgreSQL You have experience of working in environments with high throughput data (millions of events per hour) You have strong Kafka experience More ❯

s Data Governance and Data Classification policies. Maintain data dictionary. Maintain business level data model. Recommending and introducing new technology where needed. Core: Cloud data platforms (e.g. Snowflake, BigQuery, Redshift) Data transformation technology such as DBT Visual Studio Code Python CI automation systems such as Jenkins A git-based source control system such as BitBucket Data Warehouse/Kimball More ❯

Bells Data Governance and Data Classification policies. Maintain data dictionary. Maintain business level data model. Recommending and introducing new technology where needed. Core: Cloud data platforms (e.g. Snowflake, BigQuery, Redshift) Data transformation technology such as DBT Visual Studio Code Python CI automation systems such as Jenkins A git-based source control system such as BitBucket Data Warehouse/Kimball More ❯

seamless data processing and analytics. Hands-on experience in data integration , including designing and optimising data pipelines (batch and streaming) and integrating cloud-based platforms (e.g., Azure Synapse, AWS Redshift, Google BigQuery ) with legacy systems, ensuring performance and scalability. Deep knowledge of ETL/ELT processes , leveraging tools like Apache Airflow, dbt, or Informatica , with a focus on ensuring More ❯

with 2+ years in a technical leadership or management role Strong technical proficiency in data modelling, data warehousing, and distributed systems Hands-on experience with cloud data services (AWS Redshift, Glue, EMR or equivalent) Solid programming skills in Python and SQL Familiarity with DevOps practices (CI/CD, Infrastructure as Code - e.g., Terraform) Excellent communication skills with both technical More ❯

. Hands-on experience with data orchestration tools (Airflow, dbt, Dagster, or Prefect). Solid understanding of cloud data platforms (AWS, GCP, or Azure) and data warehousing (Snowflake, BigQuery, Redshift). Experience with streaming technologies (Kafka, Kinesis, or similar). Strong knowledge of data modelling, governance, and architecture best practices . Excellent leadership, communication, and stakeholder management skills. NICE More ❯