1 to 25 of 83 Hadoop Jobs in London

design and data modeling experience, and advanced SQL across large, complex datasets. Extensive experience with big data technologies and cloud data warehouses (Snowflake, BigQuery, Hadoop/Hive) at terabyte-and-growing scale. Experience building reusable frameworks, platforms, or tooling that other engineers depend on (not just point solutions).Hands ...

Django, Docker Experience working with ETL pipelines is desirable e.g. Luigi, Airflow or Argo Experience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc. Data acquisition and development of data sets and improving data quality Preparing data for predictive and prescriptive modelling Reporting tools (e.g. Tableau, PowerBI ...

experience in their technologies You have experience in database technologies including writing complex queries against their (relational and non-relational) data stores (e.g. Postgres, Hadoop, Elasticsearch, Graph databases), and designing the database schemas to support those queries You have a good understanding of coding best practices and design patterns ...

Science or a related field is preferred. Preferred Skills Familiarity with cloud services like AWS, Google Cloud, or Azure. Experience with big data tools: Hadoop, Spark, Kafka, etc. Knowledge of data integration tools like Informatica, Talend, etc. Understanding of machine learning algorithms and statistics. Experience with data visualization tools ...

experience building, scaling, and shipping large-scale data platforms in production. Deep practical experience with distributed data processing frameworks (e.g., Apache Spark, Flink, Hadoop). Strong expertise in message brokers and event streaming platforms (e.g., Apache Kafka, Kinesis). End-to-end exposure to data pipeline lifecycle development, including ...

experience building, scaling, and shipping large-scale data platforms in production. Deep practical experience with distributed data processing frameworks (e.g., Apache Spark, Flink, Hadoop).Strong expertise in message brokers and event streaming platforms (e.g., Apache Kafka, Kinesis).End-to-end exposure to data pipeline lifecycle development, including extensive experience ...

Databases and Data Warehousing concepts. 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. Qualifications You must be: Willing to work on client sites ...

scale ETL development. Proficiency in SQL and programming languages such as Python, Java, or Scala. Hands‐on experience with big data frameworks (e.g., Spark, Hadoop, Flink) and cloud platforms (AWS, GCP, or Azure). Familiarity with data warehouse technologies (e.g., Snowflake, Redshift, BigQuery) and workflow orchestration tools (e.g., Airflow ...

Python, SQL, JavaCommercial experience in client-facing projects is a plus, especially within multi-disciplinary teamsDeep 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 ...

comparable technologies. Examples of technologies in scope include: Data processing and logic: ANSI SQL, Apache Spark, Kafka Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ Engineering and deployment: CI/CD tooling, containerised or Kubernetes-based deployment approaches where relevant You will also work with ...

Experience with data warehousing concepts and technologies (e.g., Azure Synapse Analytics, Azure Data Lake Storage). Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark) is a plus. Knowledge of data governance and data security best practices. Strong problem-solving skills and attention to detail. Excellent communication ...

comparable technologies. Examples of technologies in scope include: Data processing and logic: ANSI SQL, Apache Spark, Kafka Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ Engineering and deployment: CI/CD tooling, containerised or Kubernetes‐based deployment approaches where relevant What We Are Looking ...

decisions and the trade-offs behind them. Demonstrable experience leading data estate modernisation, such as moving from legacy data warehouses, on-premises databases or Hadoop to modern cloud or hybrid data platforms. Deep expertise in at least one modern data platform (Databricks, Snowflake or Microsoft Fabric), plus a working ...

Server, PostgreSQL, Oracle, MySQL; Data visualisation software: Spotfire, Tableau, or Power BI; Azure cloud computing platform; Big data technologies such as Spark, Elasticsearch, Hadoop; Statistical techniques (regression, clustering etc.); Machine learning and pattern recognition; Front-end web development e.g. HTML, JavaScript; We are looking for tenacious and curious individuals ...

degree, or experience in a professional field or military- Experience as technical specialist in design and architecture- Experience in database (eg. SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis)- Experience in consulting, design and implementation of serverless distributed solutions- Experience in cloud based solution (AWS or equivalent), system, network and operating ...

solutions on at least one public cloud platform (AWS, Azure, or GCP)Working knowledge of distributed data systems: Apache Spark, Delta Lake, or equivalent (Hadoop, Kafka, Flink)Experience leading technical customer conversations — discovery, whiteboarding, architecture reviewsFamiliarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data ...

solutions on at least one public cloud platform (AWS, Azure, or GCP)Working knowledge of distributed data systems: Apache Spark, Delta Lake, or equivalent (Hadoop, Kafka, Flink)Experience leading technical customer conversations — discovery, whiteboarding, architecture reviewsFamiliarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data ...

solutions on at least one public cloud platform (AWS, Azure, or GCP) Working knowledge of distributed data systems: Apache Spark, Delta Lake, or equivalent (Hadoop, Kafka, Flink) Experience leading technical customer conversations - discovery, whiteboarding, architecture reviews Familiarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming ...

infrastructure underpinning data systems through a DevOps approach Knowledge of handling and transforming various data types (JSON, CSV, etc) with Apache Spark, Databricks or Hadoop Good understanding of the possible architectures involved in modern data system design (e.g. Data Warehouse, Data Lakes and Data Meshes) and the different ...

such as Pulsar, Kafka, RedPanda. Must have knowledge and experience automatically provisioning and supporting multiple big data technologies; such as MongoDB, DataBricks, Big Query, Hadoop, Spark, Trino, DataLakes/Lakehouse Architectures. Infrastructure management with Terraform Experience with Github or any other source control Knowledge of Scripting/Coding languages ...

testing Required Skills and Experience Strong experience with at least two of the following platforms: Cloudera Elastic Stack Dataiku DSS Good knowledge of Hadoop ecosystem technologies such as HDFS, YARN, Hive, Spark and Kafka Experience with Elasticsearch, Logstash, Kibana and Beats Experience designing data pipelines, ETL processes and streaming ...

/ML offerings. Strong programming skills (Python, Java, C++) and experience with DevOps practices (CI/CD). Knowledge of big data technologies (Hadoop, Spark, Flink). Knowledge of NoSQL databases (MongoDB, Cassandra, DynamoDB), message queueing systems (Kafka, RabbitMQ), and version control systems (Git). Preferred Skills: Hugging Face ...

production. Strong knowledge of Python and core ML libraries (e.g., spaCy, NumPy, SciPy, Transformers). Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP). Deep expertise in MLOps principles, scalable model deployment, and model governance. Excellent communication and stakeholder management skills with the ability ...

experience (focus on machine learning): SQL and Python’s Data Science stack; good knowledge of at least one big data framework (e.g., PySpark, Hive, Hadoop) is a plus. R, SPSS, and SAS are considered nice-to-have Strong understanding of machine learning methods and experience applying them to complex ...

Life Sciences. Experience working for a software vendor, SaaS provider, or professional services organisation. Familiarity with Ataccama or similar enterprise data platforms. Exposure to Hadoop/Spark, NoSQL, XSLT, Hive, Kafka, Snowflake, or Databricks. Python or other programming languages. Familiarity with SDLC, code management and upgrades, and Unix/ ...