9 of 9 Hadoop Jobs in the South West

Clear communicator, able to translate complex data concepts to cross-functional teams Bonus points for experience with: DevOps tools like Docker, Kubernetes, CI/CD Big data tools (Spark, Hadoop), ETL workflows, or high-throughput data streams Genomic data formats and tools Cold and hot storage management, ZFS/RAID systems, or tape storage AI/LLM tools to More ❯

as Code (IaC) and deploying infrastructure across environments Managing cloud infrastructure with a DevOps approach Handling and transforming various data types (JSON, CSV, etc.) using Apache Spark, Databricks, or Hadoop Understanding modern data system architectures (Data Warehouse, Data Lakes, Data Meshes) and their use cases Creating data pipelines on cloud platforms with error handling and reusable libraries Documenting and More ❯

Terraform, Jenkins, Bamboo, Concourse etc. Monitoring utilising products such as: Prometheus, Grafana, ELK, filebeat etc. Observability - SRE Big Data solutions (ecosystems) and technologies such as: Apache Spark and the Hadoop Ecosystem Edge technologies e.g. NGINX, HAProxy etc. Excellent knowledge of YAML or similar languages The following Technical Skills & Experience would be desirable for Data Devops Engineer: Jupyter Hub Awareness More ❯

Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model Evaluation and Validation Big Data Technologies (e.g., Spark, Hadoop - conceptual understanding) Database Querying (e.g., SQL) Cloud-based Data Platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML) Ethics in Data Science and AI Person Specification: Experience supporting data More ❯

Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model Evaluation and Validation Big Data Technologies (e.g., Spark, Hadoop - conceptual understanding) Database Querying (e.g., SQL) Cloud-based Data Platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML) Ethics in Data Science and AI Person Specification: Experience supporting data More ❯

innovation. What You'll Bring Experience with SQL (e.g. MS SQL, Oracle) and NoSQL (e.g. MongoDB, Neo4J) Strong skills in Python, ETL, API integration, and data processing Familiarity with Hadoop, Docker, and containerisation technologies Knowledge of Generative AI, Natural Language Processing, or OCR is a plus Understanding of data governance and compliance in sensitive environments Eligibility for SC clearance More ❯

innovation. What You'll Bring Experience with SQL (e.g. MS SQL, Oracle) and NoSQL (e.g. MongoDB, Neo4J) Strong skills in Python, ETL, API integration, and data processing Familiarity with Hadoop, Docker, and containerisation technologies Knowledge of Generative AI, Natural Language Processing, or OCR is a plus Understanding of data governance and compliance in sensitive environments Eligibility for SC clearance More ❯

and CI/CD paradigms and systems such as: Ansible, Terraform, Jenkins, Bamboo, Concourse etc. Observability - SRE Big Data solutions (ecosystems) and technologies such as: Apache Spark and the Hadoop Ecosystem Excellent knowledge of YAML or similar languages The following Technical Skills & Experience would be desirable: Jupyter Hub Awareness RabbitMQ or other common queue technology e.g. ActiveMQ NiFi Rego More ❯

Expertise in implementing statistical techniques in production-grade code, with a focus on scalability and reliability. Experience with large-scale data analysis, manipulation, and distributed computing platforms (e.g., Hive, Hadoop). Familiarity with advanced machine learning methods, including neural networks, reinforcement learning, and other cutting-edge Gen AI approaches. Skilled in API development and deployment, with a focus on More ❯