8 of 8 Apache Spark Jobs in Hampshire

Capture, Technical Leadership, Architectural Patterns (OO, Cloud-native, Microservices), Database Design, Application Security Testing. Cloud & Infrastructure: AWS, Azure, VMWare usage and general knowledge. Data & Integration: Data Pipeline Orchestration (Airflow, Spark, NiFi etc.), Message Brokers (Kafka), Object Storage (S3, MinIO), API Management, Python Scripting. Automation & Low-Code: Low-code Platforms (Power Platform, Appian, UiPath etc.), RPA solutions, Business Process Analysis. More ❯

Ops. Ideally, you’ll also be technically skilled in most or all of the below: Expert knowledge of Python and SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning More ❯

DataOps methodologies and tools, including experience with CI/CD pipelines, containerisation, and workflow orchestration. Familiar with ETL/ELT frameworks, and experienced with Big Data Processing Tools (e.g. Spark, Airflow, Hive, etc.) Knowledge of programming languages (e.g. Java, Python, SQL) Hands-on experience with SQL/NoSQL database design Degree in STEM, or similar field; a Master’s More ❯

models encompassing data from traffic management sensors and other data sources Experience working with large data sets, experience working with distributed computing a plus (Map/Reduce, Hadoop, Hive, Apache Spark, etc.) Develop advanced data reporting capabilities and data visualizations; combining multiple disparate data sources to gain detailed insights into photo enforcement programs. Provide architectural expertise, direction, and More ❯

part of an Agile engineering or development team Strong hands-on experience and understanding of working in a cloud environment such as AWS Experience with EMR (Elastic Map Reduce), Spark Strong experience with CI/CD pipelines with Jenkins Experience with the following technologies: SpringBoot, Gradle, Terraform, Ansible, GitHub/GitFlow, PCF/OCP/Kubernetes technologies, Artifactory, IaC 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 More ❯

data engineering or data warehousing and/or a similar data developing role required Experience using Azure Data Factory, Microsoft SSIS, or other leading ETL tools preferred. Experience with Spark, Hadoop, or cloud technologies preferred PHYSICAL DEMANDS AND WORKING ENVIRONMENT The physical demands and work environment are representative of those that must be met or encountered to successfully perform More ❯

all of the below is not required. Programming Languages: Python, Java Frameworks/Libraries: Spring (Boot, Batch, Cache, Web), Cucumber (Testing framework for BDD), Drools (Business rules management system), Spark (Big Data processing framework) Build/CI/CD Tools: Gradle (Build automation tool), Jenkins (CI/CD automation server) Configuration Management/Infrastructure as Code: Ansible, Terraform Cloud More ❯