1 to 25 of 43 Apache Jobs in the East of England

field. Technical Skills Required Hands-on software development experience with Python and experience with modern software development and release engineering practices (e.g. TDD, CI/CD). Experience with Apache Spark or any other distributed data programming frameworks. Comfortable writing efficient SQL and debugging on cloud warehouses like Databricks SQL or Snowflake. Experience with cloud infrastructure like AWS or More ❯

such as AWS, Azure, or GCP · Docker · Documenting code, architectures, and experiments · Linux systems and bash terminals Preferred Qualifications o Databases and/or data warehousing technologies, such as Apache Hive, Iceberg etc. o Data transformation via SQL and DBT. o Orchestration platforms such as Apache Airflow, Argo Workflows, etc. o Data catalogs and metadata management tools o More ❯

Computing like Spark/KStreams/Kafka. Connect and Streaming Frameworks such as Kafka. Knowledge on Monolithic versus Microservice Architecture concepts for building large-scale applications. Familiar with the Apache suite including Hadoop modules such as HDFS, Yarn, HBase, Hive, Spark as well as Apache NiFi. Familiar with containerization and orchestration technologies such as Docker, Kubernetes. Familiar with More ❯

data warehousing concepts and data modeling . Excellent problem-solving and communication skills focused on delivering high-quality solutions. Understanding or hands-on experience with orchestration tools such as Apache Airflow . Deep knowledge of non-functional requirements such as availability , scalability , operability , and maintainability . #J-18808-Ljbffr More ❯

of data warehousing concepts and data modeling. Excellent problem-solving and communication skills focused on delivering high-quality solutions. Understanding or hands-on experience with orchestration tools such as Apache Airflow. Deep knowledge of non-functional requirements such as availability, scalability, operability, and maintainability. Seniority level Seniority level Mid-Senior level Employment type Employment type Full-time Job function More ❯

HuggingFace, scikit-learn, PyTorch, Pandas, NumPy, SciPy Experience with AWS (principally EC2, S3, SageMaker) or Azure/GCP equivalents Some experience of designing, developing and deploying scalable infrastructure (eg Apache Airflow, Luigi or other cluster management software) Object Orientated concepts and design The ability to design and build unit-tested and well documented modular code Understanding of Agile software More ❯

skills in system testing, QA practices, and troubleshooting methodologies. Familiarity with integration tools (ESB, Web Services, WebSphere, SOAP). Knowledge of infrastructure: Windows Server/Client, RDS, IIS/Apache/WebSphere, web browsers. Experience in system upgrades, migrations, and CI/CD (Microsoft DevOps). Proficient in SQL, XML, and basic scripting (Python/JavaScript). Able to More ❯

skills in system testing, QA practices, and troubleshooting methodologies. Familiarity with integration tools (ESB, Web Services, WebSphere, SOAP). Knowledge of infrastructure: Windows Server/Client, RDS, IIS/Apache/WebSphere, web browsers. Experience in system upgrades, migrations, and CI/CD (Microsoft DevOps). Proficient in SQL, XML, and basic scripting (Python/JavaScript). Able to More ❯

a bonus) SQL PySpark Delta Lake Bash (both CLI usage and scripting) Git Markdown Scala (bonus, not compulsory) Azure SQL Server as a HIVE Metastore (bonus) Technologies: Azure Databricks Apache Spark Delta Tables Data processing with Python PowerBI (Integration/Data Ingestion) JIRA Due to the nature and urgency of this post, candidates holding or who have held high More ❯

built features Integrate and extend Codice Alliance tools with the existing catalogue Build secure and modular services for ingesting, indexing, and querying geospatial and imagery data Work with OSGi, Apache Karaf, and other modular Java platforms Ensure compliance with data security, access control, and audit requirements Create Design and Build documentation derived from customer requirements. Required Experience: Strong Java … development experience, especially in modular or distributed systems Familiarity with OSGi, Apache Karaf, and the DDF architecture Experience with REST APIs, secure data handling, and geospatial data formats Experience with build tools (Maven), version control (Git), and CI/CD pipelines Experience with geospatial standards (OGC, GeoJSON, WKT, etc.) Knowledge of Elasticsearch, Solr, or other search indexing tools Familiarity More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

Engineering (open to professionals from various data eng. backgrounds — data pipelines, ML Eng, data warehousing, analytics engineering, big data, cloud etc.) Technical Exposure: Experience with tools like SQL, Python, Apache Spark, Kafka, Cloud platforms (AWS/GCP/Azure), and modern data stack technologies Formal or Informal Coaching Experience: Any previous coaching, mentoring, or training experience — formal or informal More ❯

and running hybrids. Container orchestration deployment, configuration, and administration. (e.g. Docker Swarm, Kubernetes). Automation and configuration management (e.g., Ansible, Puppet, Chef). Analytics infrastructure and tooling (e.g. Databricks, Apache Spark). Technical leadership, championing standard methodologies, and leading technical product transformations. In Return: We are proud to have a set of behaviours that reflect our culture and guide More ❯

and running hybrids. Container orchestration deployment, configuration, and administration. (e.g. Docker Swarm, Kubernetes). Automation and configuration management (e.g., Ansible, Puppet, Chef). Analytics infrastructure and tooling (e.g. Databricks, Apache Spark). Technical leadership, championing standard methodologies, and leading technical product transformations. In Return We are proud to have a set of behaviours that reflect our culture and guide More ❯

timely releases. Proven experience managing Power BI deployments (including workspaces, datasets, and reports). Strong understanding of data pipeline deployment using tools like Azure Data Factory, AWS Glue, or Apache Airflow. Hands-on experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins). 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 Equal 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 Equal More ❯