1 to 25 of 351 Permanent Apache Airflow Jobs

production issues. Optimize applications for performance and responsiveness. Stay Up to Date with Technology: Keep yourself and the team updated on the latest Python technologies, frameworks, and tools like Apache Spark, Databricks, Apache Pulsar, Apache Airflow, Temporal, and Apache Flink, sharing knowledge and suggesting improvements. Documentation: Contribute to clear and concise documentation for software, processes … Experience with cloud platforms like AWS, GCP, or Azure. DevOps Tools: Familiarity with containerization (Docker) and infrastructure automation tools like Terraform or Ansible. Real-time Data Streaming: Experience with Apache Pulsar or similar systems for real-time messaging and stream processing is a plus. Data Engineering: Experience with Apache Spark, Databricks, or similar big data platforms for processing … large datasets, building data pipelines, and machine learning workflows. Workflow Orchestration: Familiarity with tools like Apache Airflow or Temporal for managing workflows and scheduling jobs in distributed systems. Stream Processing: Experience with Apache Flink or other stream processing frameworks is a plus. Desired Skills Asynchronous Programming: Familiarity with asynchronous programming tools like Celery or asyncio. Frontend Knowledge More ❯

robust way possible! Diverse training opportunities and social benefits (e.g. UK pension schema) What do you offer? Strong hands-on experience working with modern Big Data technologies such as Apache Spark, Trino, Apache Kafka, Apache Hadoop, Apache HBase, Apache Nifi, Apache Airflow, Opensearch Proficiency in cloud-native technologies such as containerization and Kubernetes More ❯

Note: This is a Sr. Level role! About the Role- We are looking for an experienced Senior Airflow Developer with over 5 years of experience to help transition our existing Windows scheduler jobs to Apache Airflow DAGs. In this role, you’ll play a critical part in modernizing and optimizing our task automation processes by converting existing … into efficient, manageable, and scalable workflows in Airflow. You will also work on security hardening, implementing data pipelines, and designing ETL processes. Key Responsibilities- Convert Windows Scheduler Jobs to Airflow: Migrate existing Windows-based scheduled jobs into Airflow DAGs, ensuring smooth execution and reliability. Develop and Optimize DAGs: Author, schedule, and monitor DAGs (Directed Acyclic Graphs) to handle … data workflows, ETL tasks, and various automation processes. Programming and Scripting: Use Python as the primary language for Airflow DAGs and task logic, with experience in SQL for data manipulation. Set Up and Configure Airflow: Provide comprehensive instructions and configurations for setting up Airflow environments, including deployment, resource allocation, and high availability. Security Hardening: Implement security best More ❯

monitor machine learning models for anomaly detection and failure prediction. Analyze sensor data and operational logs to support predictive maintenance strategies. Develop and maintain data pipelines using tools like Apache Airflow for efficient workflows. Use MLflow for experiment tracking, model versioning, and deployment management. Contribute to data cleaning, feature engineering, and model evaluation processes. Collaborate with engineers and … science libraries (Pandas, Scikit-learn, etc.). Solid understanding of machine learning concepts and algorithms . Interest in working with real-world industrial or sensor data . Exposure to Apache Airflow and/or MLflow (through coursework or experience) is a plus. A proactive, analytical mindset with a willingness to learn and collaborate. Why Join Us Work on More ❯

data from various sources to data warehouses. - **Programming Expertise:** A solid understanding of Python, PySpark, and SQL is required to manipulate and analyze data efficiently. - **Knowledge of Spark and Airflow:** In-depth knowledge of Apache Spark for big data processing and Apache Airflow for orchestrating complex workflows is essential for managing data pipelines. - **Cloud-Native Services More ❯

delivering end-to-end AI/ML projects. Nice to Have: Exposure to LLMs (Large Language Models), generative AI , or transformer architectures . Experience with data engineering tools (Spark, Airflow, Snowflake). Prior experience in fintech, healthtech, or similar domains is a plus. More ❯

Snowflake, Databricks) Strong DevOps mindset with experience in CI/CD pipelines, monitoring, and observability tools (Grafana or equivalent). Exposure to analytics, reporting, and BI tools such as Apache Superset, Lightdash or OpenSearch Willingness to work across the stack by contributing to API development and, at times, UI components (Vue.js, Zoho, or similar). Excellent communication and collaboration More ❯

will do Design, build, and maintain scalable, cloud-based data pipelines and architectures to support advanced analytics and machine learning initiatives. Develop robust ELT workflows using tools like dbt, Airflow, and SQL (PostgreSQL, MySQL) to transform raw data into high-quality, analytics-ready datasets. Collaborate with data scientists, analysts, and software engineers to ensure seamless data integration and availability … Azure. AWS experience is considered, however Azure exposure is essential. Data Warehousing: Proven expertise with Snowflake – schema design, performance tuning, data ingestion, and security. Workflow Orchestration: Production experience with Apache Airflow (Prefect, Dagster or similar), including authoring DAGs, scheduling workloads and monitoring pipeline execution. Data Modeling: Strong skills in dbt, including writing modular SQL transformations, building data models More ❯

will do Design, build, and maintain scalable, cloud-based data pipelines and architectures to support advanced analytics and machine learning initiatives. Develop robust ELT workflows using tools like dbt, Airflow, and SQL (PostgreSQL, MySQL) to transform raw data into high-quality, analytics-ready datasets. Collaborate with data scientists, analysts, and software engineers to ensure seamless data integration and availability … Azure. AWS experience is considered, however Azure exposure is essential. Data Warehousing: Proven expertise with Snowflake – schema design, performance tuning, data ingestion, and security. Workflow Orchestration: Production experience with Apache Airflow (Prefect, Dagster or similar), including authoring DAGs, scheduling workloads and monitoring pipeline execution. Data Modeling: Strong skills in dbt, including writing modular SQL transformations, building data models More ❯

.Understanding of CI/CD pipelines, version control (Git), and Agile methodologies.Excellent analytical, problem-solving, and communication skills.Preferred SkillsExperience with data engineering, ETL workflows, or big data frameworks (Spark, Airflow).Knowledge of machine learning libraries (NumPy, Pandas, Scikit-learn, TensorFlow, etc.) is a plus.Exposure to DevOps practices, infrastructure as code, and monitoring tools (Jenkins, Terraform, Prometheus).Familiarity with security More ❯

experience working with SQL and databases/engines such as MySQL, PostgreSQL, SQL Server, Snowflake, Redshift, Presto, etc Experience building ETL and stream processing pipelines using Kafka, Spark, Flink, Airflow/Prefect, etc. Familiarity with data science stack: e.g. Juypter, Pandas, Scikit-learn, Dask, Pytorch, MLFlow, Kubeflow, etc. Strong experience with using AWS/Google Cloud Platform (S3S, EC2E More ❯

of the programming languages like Python, C++[2] , Java ● Experience with version control and code review tools such as Git ● Knowledge of latest data pipeline orchestration tools such as Airflow ● Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (e.g., Docker, Terraform, CloudFormation). ● Familiarity with data quality, data governance, and observability tools (e.g., Great More ❯

of the programming languages like Python, C++[2] , Java ● Experience with version control and code review tools such as Git ● Knowledge of latest data pipeline orchestration tools such as Airflow ● Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (e.g., Docker, Terraform, CloudFormation). ● Familiarity with data quality, data governance, and observability tools (e.g., Great More ❯

and Responsibilities While in this position your duties may include but are not limited to: Support the design, development, and maintenance of scalable data pipelines using tools such as Apache Airflow, dbt, or Azure Data Factory. Learn how to ingest, transform, and load data from a variety of sources, including APIs, databases, and flat files. Assist in the More ❯

you the chance to work on cutting-edge solutions that make a real impact.Key Responsibilities* Data Engineering: Design and implement data pipelines, lakes, and warehouses using tools like Spark, Airflow, or dbt.* API & Microservices Development: Build secure, efficient APIs and microservices for data integration.* Full Stack Development: Deliver responsive, high-performance web applications using React (essential), plus Angular or More ❯

familiarity with automated testing or CI/CD pipelines Bachelor's degree in Computer Science, Software Engineering, or a related field Bonus Skills Knowledge of data orchestration tools (e.g. Airflow) Background in Java or distributed data systems Prior exposure to large-scale infrastructure in data-heavy environments (e.g. trading, analytics, or research) Master’s degree in a technical discipline More ❯

familiarity with automated testing or CI/CD pipelines Bachelor's degree in Computer Science, Software Engineering, or a related field Bonus Skills Knowledge of data orchestration tools (e.g. Airflow) Background in Java or distributed data systems Prior exposure to large-scale infrastructure in data-heavy environments (e.g. trading, analytics, or research) Master’s degree in a technical discipline More ❯

Profile: Proven experience as a Data Engineer, with strong expertise in designing and managing large-scale data systems. Hands-on proficiency with modern data technologies such as Spark, Kafka, Airflow, 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 More ❯

Profile: Proven experience as a Data Engineer, with strong expertise in designing and managing large-scale data systems. Hands-on proficiency with modern data technologies such as Spark, Kafka, Airflow, 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 More ❯

following engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith More ❯

in C# and Python with additional skills in Java, JavaScript/Typescript, Angular, very strong SQL, Windows server, UNIX, and .Net. Strong research skills. Strong experience of Terraform, AWS, Airflow, Docker, Github/Github actions, Jenkins/Teamcity• Strong AWS specific skills for Athena, Lambda, ECS, ECR, S3 and IAM Strong knowledge in industry best practices in development and More ❯

move us towards our vision of scaling up through product led growth. This role will be focused on our backend system (Symfony, PHP) and our data products (BigQuery, DBT, Airflow), but there will be opportunities to work across the platform including, agentic AI (Python, Langchain), frontend (React, TypeScript), the APIs (GraphQL, REST), our integration tool of choice (Tray.ai) and More ❯

data modeling (star schema, snowflake schema). Version Control Practical experience with Git (branching, merging, pull requests). Preferred Qualifications (A Plus) Experience with a distributed computing framework like Apache Spark (using PySpark). Familiarity with cloud data services ( AWS S3/Redshift, Azure Data Lake/Synapse, or Google BigQuery/Cloud Storage ). Exposure to workflow orchestration … tools ( Apache Airflow, Prefect, or Dagster ). Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field. More ❯

data modeling (star schema, snowflake schema). Version Control Practical experience with Git (branching, merging, pull requests). Preferred Qualifications (A Plus) Experience with a distributed computing framework like Apache Spark (using PySpark). Familiarity with cloud data services ( AWS S3/Redshift, Azure Data Lake/Synapse, or Google BigQuery/Cloud Storage ). Exposure to workflow orchestration … tools ( Apache Airflow, Prefect, or Dagster ). Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field. More ❯

Role Within the Kingdom Work closely with stakeholders to understand their data needs and design scalable solutions Build, maintain and optimise data pipelines and models using SQL, Python and Airflow Design and develop BI and reporting products such as Looker models, dashboards and data visualisations Contribute to our data modelling standards and best practices to ensure quality, reliability and … Thrills Strong SQL skills, able to write complex and performant queries with ease. Solid experience in Python development for data workflows Experience building and maintaining ETL pipelines, ideally with Apache Airflow or a similar orchestration tool Hands-on experience with Google Cloud Platform (BigQuery, GCS, etc.) or another major cloud provider Good understanding of data modelling principles and More ❯