1 to 25 of 47 Apache Airflow Jobs in the North West

Deep understanding in software architecture, object-oriented design principles, and data structures Extensive experience in developing microservices using Java, Python Experience in distributed computing frameworks like - Hive/Hadoop, Apache Spark. Good experience in Test driven development and automating test cases using Java/Python Experience in SQL/NoSQL (Oracle, Cassandra) database design Demonstrated ability to be proactive … HR related applications Experience with following cloud services: AWS Elastic Beanstalk, EC2, S3, CloudFront, RDS, DynamoDB, VPC, Elastic Cache, Lambda Working experience with Terraform Experience in creating workflows for Apache Airflow About Roku Roku pioneered streaming to the TV. We connect users to the streaming content they love, enable content publishers to build and monetize large audiences, and More ❯

Azure Storage, Medallion Architecture, and working with data formats such as JSON, CSV, and Parquet. • Strong understanding of IT concepts, including security, IAM, Key Vault, and networking. • Exposure to Apache Airflow and DBT is a bonus. • Familiarity with agile principles and practices. • Experience with Azure DevOps pipelines. The "Nice to Haves": • Certification in Azure or related technologies. • Experience More ❯

But if not? We’ll help you get there: Understanding of cloud computing security concepts Proficiency with IaC tools like Terraform or CloudFormation. Experience with workflow orchestration tools (e.g., Airflow, Dagster). Good understanding of Cloud providers – AWS, Microsoft Azure, Google Cloud Familiarity with DBT, Delta Lake, Databricks Experience working in Agile environments with tools like Jira and Git. More ❯

But if not? We’ll help you get there: Understanding of cloud computing security concepts Proficiency with IaC tools like Terraform or CloudFormation. Experience with workflow orchestration tools (e.g., Airflow, Dagster). Good understanding of Cloud providers – AWS, Microsoft Azure, Google Cloud Familiarity with DBT, Delta Lake, Databricks Experience working in Agile environments with tools like Jira and Git. More ❯

But if not? We’ll help you get there: Understanding of cloud computing security concepts Proficiency with IaC tools like Terraform or CloudFormation. Experience with workflow orchestration tools (e.g., Airflow, Dagster). Good understanding of Cloud providers – AWS, Microsoft Azure, Google Cloud Familiarity with DBT, Delta Lake, Databricks Experience working in Agile environments with tools like Jira and Git. More ❯

systems (e.g. Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the More ❯

systems (e.g. Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the More ❯

systems (e.g. Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the More ❯

best practices. Automation & Monitoring: Implement and support automated monitoring systems to detect data anomalies, system failures, and performance issues and leverage advanced scripting and orchestration tools (e.g., Python, Bash, Apache Airflow) to automate workflows and reduce operational overhead. Root Cause Analysis & Incident Management: Lead post-incident reviews, perform root cause analysis for data disruptions, and implement corrective actions More ❯

best practices. Automation & Monitoring: Implement and support automated monitoring systems to detect data anomalies, system failures, and performance issues and leverage advanced scripting and orchestration tools (e.g., Python, Bash, Apache Airflow) to automate workflows and reduce operational overhead. Root Cause Analysis & Incident Management: Lead post-incident reviews, perform root cause analysis for data disruptions, and implement corrective actions More ❯

MySQL, PostgreSQL, or Oracle Experience with big data technologies such as Hadoop, Spark, or Hive Familiarity with data warehousing and ETL tools such as Amazon Redshift, Google BigQuery, or Apache Airflow Proficiency in Python and at least one other programming language such as Java, or Scala Willingness to mentor more junior members of the team Strong analytical and More ❯

environment using agile methodologies with operational targets crossing multiple departments Nice-to-have experience: Infrastructure as code (Terraform, cloud formation etc...) Cloud platforms (Aws, Azure, GCP etc..) Data orchestration (Airflow, Dagster etc...) The Team The Data & Analytics department serves as the central hub for data engineering and insights across our organization, comprising four essential teams: Data Engineering, Reporting, Commercial More ❯

architectures, data pipelines, and ETL processes Hands-on experience with cloud platforms (GCP, AWS, Azure) and their data-specific services Proficiency in Python, SQL, and data orchestration tools (e.g., Airflow, DBT) Experience with modern data warehouse technologies (BigQuery, Snowflake, Redshift, etc.) Strong understanding of data modeling, data governance, and data quality principles Excellent communication skills with the ability to More ❯

AND EXPERIENCE: A successful Data Engineer will have the following skills and experience: Ability and experience interacting with key stakeholders Strong experience in SQL/Python Good understanding of Airflow/DBT Experience with GCP/AWS Background in CI/CD THE BENEFITS: You will receive a salary, dependent on experience. Salary is up to £60,000. On More ❯

will need experience in the following: A track record of consolidating data sources, creating pipelines, and ideally a software engineering mindset. Excellent experience with Containers. Strong knowledge of Python, Airflow, SQL, and Linux. Proficient in AWS Lambda, ECS, Athena, S3, Kinesis, Glue, CloudWatch/Terraform/CDK, Tableau Online. They are particularly keen on Senior Data Engineers who utilize More ❯

of machine learning algorithms, time series forecasting, and model evaluation Familiarity with cloud platforms (AWS and/or GCP) Experience with productionising models using tools like Kubernetes, Docker, and Airflow Good knowledge of NLP techniques for text analysis Ability to design and run effective experiments (A/B, multivariate) Excellent communicator – able to explain complex ideas to both technical More ❯

scale datasets. Demonstrated ability to develop and deploy Feature Engineering and Modeling applications to data platforms built on Databricks or similar platforms and platform components (e.g., Snowflake, ML Flow, Airflow, etc.). Demonstrated experience in using Azure-based cloud applications, services and infrastructure or significant, transferrable experience with other Cloud Providers (e.g., AWS or GCP). Comfortable creating data More ❯

modeling, and predictive analytics. Excellent communication and stakeholder management skills. Desirable: Experience working with large-scale retail datasets (e.g., POS, CRM, supply chain). Familiarity with tools like dbt, Airflow, or MLflow. Master’s or PhD in Data Science, Statistics, Computer Science, or related fields. Benefits: Competitive salary and performance bonuses Flexible working options, including hybrid remote work More ❯

Cloud Platform stack (BigQuery, Composer, Dataplex, Dataflow, Cloud Functions, Cloud Run, Pub/Sub, GCS, Vertex AI, GKE) or similar cloud platforms Familiarity with open-source data-stack tools (Airflow, DBT, Kafka, Great Expectation, etc.) Appreciation of modern cloud data stack, headless BI, analytics engineering, Data Mesh, and Lake House Although not essential, it would be great if you More ❯

AWS and Azure cloud services to provide the necessary infrastructure, resources, and interfaces for data loading and LLM workflows. Use Python and large-scale data workflow orchestration platforms (e.g. Airflow) to build software artifacts for ETL, integrating diverse data formats and storage technologies, and incorporate them into robust data workflows and dynamic systems Design and develop APIs (e.g. using More ❯

are some technologies that our Data Scientists use (we don't expect applicants to have experience with all of these): Python and Databricks for Data Science, Spark, MLFlow and Airflow for ML Workflows, Google Cloud Platform for our analytics infrastructure, dbt and BigQuery for data modelling and warehousing. We are looking to grow our diverse team of curious data More ❯

platform, ensuring scalability, reliability, and security. Drive modernisation by transitioning from legacy systems to a lean, scalable platform. Act as a lead expert for technologies such as AWS, DBT, Airflow, and Databricks. Establish best practices for data modelling, ingestion, storage, streaming, and APIs. Governance & Standards Ensure all technical decisions are well-justified, documented, and aligned with business needs. Lead … in data engineering and cloud engineering, including data ingestion, transformation, and storage. Significant hands-on experience with AWS and its data services. Expert-level skills in SQL, Python, DBT, Airflow and Redshift. Confidence in coding, scripting, configuring, versioning, debugging, testing, and deploying. Ability to guide and mentor others in technical best practices. A product mindset, focusing on user needs More ❯

using python (specifically pyspark) and Node.js for processing data, backed by various Hadoop stack technologies such as HDFS and HBase. MongoDB and Elasticsearch are used for indexing smaller datasets. Airflow & Nifi are used to co-ordinate the processing of data, while Jenkins, Jira, Confluence and Github are used as support tools. We use ansible to manage configuration and deployments. … for processing data You will be using Hadoop stack technologies such as HDFS and HBase Experience using MongoDB and Elasticsearch for indexing smaller datasets would be beneficial Experience using Airflow & Nifi to co-ordinate the processing of data would be beneficial You will be using Ansible to manage configuration and deployments Salary And Benefits Salary DOE 25 days annual More ❯

platform, ensuring scalability, reliability, and security. Drive modernisation by transitioning from legacy systems to a lean, scalable platform. Act as a lead expert for technologies such as AWS, DBT, Airflow, and Databricks. Establish best practices for data modelling, ingestion, storage, streaming, and APIs. Governance & Standards Ensure all technical decisions are well-justified, documented, and aligned with business needs. Lead … in data engineering and cloud engineering, including data ingestion, transformation, and storage. Significant hands-on experience with AWS and its data services. Expert-level skills in SQL, Python, DBT, Airflow and Redshift. Confidence in coding, scripting, configuring, versioning, debugging, testing, and deploying. Ability to guide and mentor others in technical best practices. A product mindset, focusing on user needs More ❯

platform, ensuring scalability, reliability, and security. Drive modernisation by transitioning from legacy systems to a lean, scalable platform. Act as a lead expert for technologies such as AWS, DBT, Airflow, and Databricks. Establish best practices for data modelling, ingestion, storage, streaming, and APIs. Governance & Standards Ensure all technical decisions are well-justified, documented, and aligned with business needs. Lead … in data engineering and cloud engineering, including data ingestion, transformation, and storage. Significant hands-on experience with AWS and its data services. Expert-level skills in SQL, Python, DBT, Airflow and Redshift. Confidence in coding, scripting, configuring, versioning, debugging, testing, and deploying. Ability to guide and mentor others in technical best practices. A product mindset, focusing on user needs More ❯