1 to 25 of 89 Permanent 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 ❯

solving skills and attention to detail. Excellent communication and collaboration skills. Preferred Qualifications: Experience with containerization and orchestration tools (e.g., Docker, Kubernetes). Knowledge of big data technologies (e.g., Hadoop, Spark). Experience in commodities (Agriculture, Natural Gas, Power). For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy 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 ❯

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 ❯

Snowflake. Understanding of cloud platform infrastructure and its impact on data architecture. Data Technology Skills: A solid understanding of big data technologies such as Apache Spark, and knowledge of Hadoop ecosystems. Knowledge of programming languages such as Python, R, or Java is beneficial. Exposure to ETL/ELT processes, SQL, NoSQL databases is a nice-to-have, providing a More ❯

DataStage, Talend and Informatica. Ingestion mechanism like Flume & Kafka. Data modelling – Dimensional & transactional modelling using RDBMS, NO-SQL and Big Data technologies. Data visualization – Tools like Tableau Big data – Hadoop eco-system, Distributions like Cloudera/Hortonworks, Pig and HIVE Data processing frameworks – Spark & Spark streaming Hands-on experience with multiple databases like PostgreSQL, Snowflake, Oracle, MS SQL Server More ❯

DataStage, Talend and Informatica. Ingestion mechanism like Flume & Kafka. Data modelling – Dimensional & transactional modelling using RDBMS, NO-SQL and Big Data technologies. Data visualization – Tools like Tableau Big data – Hadoop eco-system, Distributions like Cloudera/Hortonworks, Pig and HIVE Data processing frameworks – Spark & Spark streaming Hands-on experience with multiple databases like PostgreSQL, Snowflake, Oracle, MS SQL Server More ❯

7+ years in data architecture and solution design, and a history of large-scale data solution implementation. Technical Expertise : Deep knowledge of data architecture principles, big data technologies (e.g., Hadoop, Spark), and cloud platforms like AWS, Azure, or GCP. Data Management Skills : Advanced proficiency in data modelling, SQL/NoSQL databases, ETL processes, and data integration techniques. Programming & Tools 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 ❯

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 ❯

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 ❯

Experience with scripting languages like Python or KornShell. Knowledge of writing and optimizing SQL queries for large-scale, complex datasets. PREFERRED QUALIFICATIONS Experience with big data technologies such as Hadoop, Hive, Spark, EMR. Experience with ETL tools like Informatica, ODI, SSIS, BODI, or DataStage. We promote an inclusive culture that empowers Amazon employees to deliver the best results for 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 ❯

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

learning through internal and external training. What you'll bring Mandatory Proficient in either GCP (Google) or AWS cloud Hands on experience in designing and building data pipelines using Hadoop and Spark technologies. Proficient in programming languages such as Scala, Java, or Python. Experienced in designing, building, and maintaining scalable data pipelines and applications. Hands-on experience with Continuous 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 ❯

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 ❯

environments (e.g. Snowflake, AWS). 6+ years of hands-on technical leadership in building large-scale, distributed data pipelines and reporting tools using big data technologies (e.g. Spark, Kafka, Hadoop), ensuring quality, scalability, and governance. Strong expertise in balancing trade-offs within complex distributed systems, focusing on data quality, performance, reliability, availability, and security. Proficient in software engineering with More ❯

architecture, schema design, and GDPR-compliant solutions Working knowledge of DevOps tools and CI/CD processes Bonus Points For Development experience in Scala or Java Familiarity with Cloudera, Hadoop, HIVE, and Spark ecosystem Understanding of data privacy regulations, including GDPR, and experience working with sensitive data Ability to learn and adapt new technologies quickly to meet business needs More ❯