1 to 25 of 85 Hadoop Jobs in London

technical concepts to non-technical stakeholders. Team Player: Ability to work effectively in a collaborative team environment, as well as independently. Preferred Qualifications: Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka). Familiarity with AWS and its data services (e.g. S3, Athena, AWS Glue). Familiarity with data warehousing solutions (e.g., Redshift, BigQuery, Snowflake). Knowledge of containerization More ❯

Azure, or Google Cloud Platform (GCP). Strong proficiency in SQL and experience with relational databases such as MySQL, PostgreSQL, or Oracle. Experience with big data technologies such as Hadoop, Spark, or Hive. Familiarity with data warehousing and ETL tools such as Amazon Redshift, Google BigQuery, or Apache Airflow. Proficiency in Python and at least one other programming language More ❯

of the following: Python, SQL, Java Commercial experience in client-facing projects is a plus, especially within multi-disciplinary teams Deep knowledge of database technologies: Distributed systems (e.g., Spark, Hadoop, EMR) RDBMS (e.g., SQL Server, Oracle, PostgreSQL, MySQL) NoSQL (e.g., MongoDB, Cassandra, DynamoDB, Neo4j) Solid understanding of software engineering best practices - code reviews, testing frameworks, CI/CD, and More ❯

Statistics, Maths or similar Science or Engineering discipline Strong Python and other programming skills (Java and/or Scala desirable) Strong SQL background Some exposure to big data technologies (Hadoop, spark, presto, etc.) NICE TO HAVES OR EXCITED TO LEARN: Some experience designing, building and maintaining SQL databases (and/or NoSQL) Some experience with designing efficient physical data More ❯

in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based data services (e.g ., AWS Redshift, Azure Synapse Analytics, Goog More ❯

in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based data services (e.g ., AWS Redshift, Azure Synapse Analytics, Goog More ❯

in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based data services (e.g ., AWS Redshift, Azure Synapse Analytics, Goog More ❯

Experience of Relational Databases and Data Warehousing concepts. Experience of Enterprise ETL tools such as Informatica, Talend, Datastage or Alteryx. Project experience using the any of the following technologies: Hadoop, Spark, Scala, Oracle, Pega, Salesforce. Cross and multi-platform experience. Team building and leading. You must be: Willing to work on client sites, potentially for extended periods. Willing to More ❯

and with customers Preferred Experience Degree in Computer Science or equivalent practical experience Commercial experience with Spark, Scala, and Java (Python is a plus) Strong background in distributed systems (Hadoop, Spark, AWS) Skilled in SQL/NoSQL (PostgreSQL, Cassandra) and messaging tech (Kafka, RabbitMQ) Experience with orchestration tools (Chef, Puppet, Ansible) and ETL workflows (Airflow, Luigi) Familiarity with cloud More ❯

and with customers Preferred Experience Degree in Computer Science or equivalent practical experience Commercial experience with Spark, Scala, and Java (Python is a plus) Strong background in distributed systems (Hadoop, Spark, AWS) Skilled in SQL/NoSQL (PostgreSQL, Cassandra) and messaging tech (Kafka, RabbitMQ) Experience with orchestration tools (Chef, Puppet, Ansible) and ETL workflows (Airflow, Luigi) Familiarity with cloud More ❯

flow diagrams, and process documentation. MINIMUM QUALIFICATIONS/SKILLS Proficiency in Python and SQL. Experience with cloud platforms like AWS, GCP, or Azure, and big data technologies such as Hadoop or Spark. Experience working with relational and NoSQL databases. Strong knowledge of data structures, data modeling, and database schema design. Experience supporting data science workloads with structured and unstructured More ❯

is important) Latest Data Science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., TensorFlow, MXNet, scikit-learn) Software engineering practices (coding standards, unit testing, version control, code review) Hadoop distributions (Cloudera, Hortonworks), NoSQL databases (Neo4j, Elastic), streaming technologies (Spark Streaming) Data manipulation and wrangling techniques Development and deployment technologies (virtualisation, CI tools like Jenkins, configuration management with Ansible More ❯

one modern scripting or programming language, such as Python, Java, Scala, or NodeJS - Experience mentoring team members on best practices PREFERRED QUALIFICATIONS - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience operating large data warehouses Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make More ❯

lakes, data lake houses and data mesh Strong understanding of best practice DataOps and MLOps Up-to-date understanding of various data engineering technologies including Apache Spark, Databricks and Hadoop Strong understanding of agile ways of working Up-to-date understanding of various programming languages including Python, Scala, R and SQL Up-to-date understanding of various databases and More ❯

distributed computing, TDD, and system design. What We're Looking For: Strong experience with Python, Spark, Scala, and Java in a commercial setting. Solid understanding of distributed systems (e.g. Hadoop, AWS, Kafka). Experience with SQL/NoSQL databases (e.g. PostgreSQL, Cassandra). Familiarity with orchestration tools (e.g. Airflow, Luigi) and cloud platforms (e.g. AWS, GCP). Passion for More ❯

distributed computing, TDD, and system design. What We're Looking For: Strong experience with Python, Spark, Scala, and Java in a commercial setting. Solid understanding of distributed systems (e.g. Hadoop, AWS, Kafka). Experience with SQL/NoSQL databases (e.g. PostgreSQL, Cassandra). Familiarity with orchestration tools (e.g. Airflow, Luigi) and cloud platforms (e.g. AWS, GCP). Passion for More ❯

across Prime Video. BASIC QUALIFICATIONS - Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets - Experience with data modeling, warehousing and building ETL 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 ❯

OpenCV. Knowledge of ML model serving infrastructure (TensorFlow Serving, TorchServe, MLflow). Knowledge of WebGL, Canvas API, or other graphics programming technologies. Familiarity with big data technologies (Kafka, Spark, Hadoop) and data engineering practices. Background in computer graphics, media processing, or VFX pipeline development. Experience with performance profiling, system monitoring, and observability tools. Understanding of network protocols, security best More ❯

Python, Go, Julia etc.) •Experience with Amazon Web Services (S3, EKS, ECR, EMR, etc.) •Experience with containers and orchestration (e.g. Docker, Kubernetes) •Experience with Big Data processing technologies (Spark, Hadoop, Flink etc) •Experience with interactive notebooks (e.g. JupyterHub, Databricks) •Experience with Git Ops style automation •Experience with ix (e.g, Linux, BSD, etc.) tooling and scripting •Participated in projects that More ❯

TensorFlow, PyTorch, XGBoost). Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume). Understanding of contact center technologies and platforms (e.g., Avaya, Genesys, Cisco, Mitel, Twilio, etc.). Understanding of the practical concerns in production machine More ❯

really make yourapplication stand out: Implementationexperience with Machine Learning models and applications Knowledgeof cloud-based Machine Learning engines (AWS, Azure, Google, etc.) Experiencewith large scale data processing tools (Spark, Hadoop, etc.) Abilityto query and program databases (SQL, No SQL) Experiencewith distributed ML frameworks (TensorFlow, PyTorch, etc.) Familiaritywith collaborative software tools (Git, Jira, etc.) Experiencewith user interface libraries/applications More ❯

utilising strong communication and stakeholder management skills when engaging with customers Significant experience of coding in Python and Scala or Java Experience with big data processing tools such as Hadoop or Spark Cloud experience; GCP specifically in this case, including services such as Cloud Run, Cloud Functions, BigQuery, GCS, Secret Manager, Vertex AI etc. Experience with Terraform Prior experience More ❯

with Spark. Experience building, maintaining, and debugging DBT pipelines. Strong proficiency in developing, monitoring, and debugging ETL jobs. Deep understanding of SQL and experience with Databricks, Snowflake, BigQuery, Azure, Hadoop, or CDP environments. Hands-on technical support experience, including escalation management and adherence to SLAs. Familiarity with CI/CD technologies and version control systems like Git. Expertise in 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 ❯