26 to 50 of 77 Apache Airflow Jobs in the North of England

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 ❯

have experience with GCP, Data Products, Data Mesh, ETL, EDW Composer, BigQuery, DataProc, DBT, Spark, Hive, Kafka, Pub-Sub, Jira, GCP Dataflow, BigTable, Python, Scala, Java, .NET, ANSI SQL, Airflow, Shell Scripts, Control-M, Git, CI & CD, HDFS, Unix File System, RDBMS, Azure DevOps, Harness. More about the role: Define Data Product Definition, Architecture, and Roadmap. Lead the delivery More ❯

description: Proficiency in GCP, Data Products, Data Mesh, ETL, EDW Composer, BigQuery, DataProc, DBT, Spark, Hive, Kafka, Pub-Sub, Jira, GCP Dataflow, BigTable, Python, Scala, Java, .NET, ANSI SQL, Airflow, Shell Scripts, Control-M, Git, CI & CD, HDFS, Unix File System, RDBMS, Azure DevOps, Harness. More about the role: Able to define Data Product Definition, Architecture, and Roadmap. Define More ❯

Expertise in modern BI and Data DW platforms such as Snowflake, BigQuery, Redshift, Power BI etc Background in ETL/ELT tooling and Data Pipelines such as DBT, Fivetran, Airflow Experienced in Cloud based solutions (Azure, AWS or Google 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 ❯

tables, formulas, and data visualization. Solid understanding of ETL concepts, data integration, and data warehousing best practices. Familiarity with version control systems (e.g., Git) and workflow orchestration tools (e.g., Airflow, dbt) is a plus. Excellent problem-solving skills and attention to detail. Strong communication and collaboration abilities. Preferred Qualifications: Experience working in agile or cross-functional teams. Knowledge of More ❯

tables, formulas, and data visualization. Solid understanding of ETL concepts, data integration, and data warehousing best practices. Familiarity with version control systems (e.g., Git) and workflow orchestration tools (e.g., Airflow, dbt) is a plus. Excellent problem-solving skills and attention to detail. Strong communication and collaboration abilities. Preferred Qualifications Experience working in agile or cross-functional teams. Knowledge of More ❯

the ability to translate data into business context A proactive, detail-oriented mindset and a drive to build systems that scale Nice to Have Experience using Azure Data Factory, Airflow, or similar orchestration tools Exposure to CI/CD pipelines and data quality testing frameworks Previous work with international logistics, supply chain data, or operational analytics Comfort working in 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 ❯

Senior Data Engineer/Data Analytics Engineer - Full Remote Working from anywhere in the UK SQL+Python+ETL+ Apache Airflow Our client is a global leading and fast growing high tech company: -Over 6,500 employees across 20+ offices; -300 Million+ active users on some of the platforms they developed; -Cutting edge AR and VR technologies, 3D printing, etc. They … will develop scalable data pipelines, ensure data quality, and support business decision-making with high-quality datasets. -Work across technology stack: SQL, Python, ETL, Big Query, Spark, Hadoop, Git, Apache Airflow, Data Architecture, Data Warehousing -Design and develop scalable ETL pipelines to automate data processes and optimize delivery -Implement and manage data warehousing solutions, ensuring data integrity through … rigorous testing and validation -Lead, plan and execute workflow migration and data orchestration using Apache Airflow -Focus on data engineering and data analytics Requirements: -5+ years of experience in SQL -5+ years of development in Python -MUST have strong experience in Apache Airflow -Experience with ETL tools, data architecture, and data warehousing solutions -Strong 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 ❯

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 ❯

Bonus points for experience with: State-of-the-art NLP models, Transformers, Agentic Approaches for mixed (temporal and text) data analysis and summarization; Experience with pipeline orchestration tools like Airflow, Argo, etc.; Proven Experience with Anomaly Detection and Forecasting with explainability for temporal and mixed data; Intermediate+ English — ability to participate in written discussions with international teams and clients. More ❯

. Technologies Used Our data platform supports industry-leading Data Science tools. While experience with all these technologies is not expected, familiarity is beneficial: Python and Databricks Spark, MLFlow, Airflow Google Cloud Platform dbt and BigQuery Examples of our data science work can be found on our engineering blog . What We’re Looking For Experience delivering data science More ❯

Data Science. These Are Some Technologies That Our Data Scientists Use (we Don’t Expect You To Have Experience With All Of These) Python and Databricks Spark, MLFlow, and Airflow for ML Workflows Google Cloud Platform for our analytics infrastructure dbt and BigQuery for data modelling and warehousing Some examples of our data science work can be found in More ❯

Data Science. These Are Some Technologies That Our Data Scientists Use (we Don't Expect You To Have Experience With All Of These) Python and Databricks Spark, MLFlow, and Airflow for ML Workflows Google Cloud Platform for our analytics infrastructure dbt and BigQuery for data modelling and warehousing Some examples of our data science work can be found in More ❯

common life sciences data acquisition software, such as Scientific Data Management Systems (SDMS) or Laboratory Information Management Systems (LIMS). Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow) and data parsing. Familiarity with cloud service models, SaaS infrastructure, and related SDLC. Familiarity with containerization and container orchestration tools (e.g., Docker, Kubernetes). ZONTAL is an More ❯

common life sciences data acquisition software, such as Scientific Data Management Systems (SDMS) or Laboratory Information Management Systems (LIMS). Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow) and data parsing. Familiarity with cloud service models, SaaS infrastructure, and related SDLC. Familiarity with containerization and container orchestration tools (e.g., Docker, Kubernetes). ZONTAL is an More ❯

common life sciences data acquisition software, such as Scientific Data Management Systems (SDMS) or Laboratory Information Management Systems (LIMS). Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow) and data parsing. Familiarity with cloud service models, SaaS infrastructure, and related SDLC. Familiarity with containerization and container orchestration tools (e.g., Docker, Kubernetes). ZONTAL is an More ❯