1 to 25 of 222 Permanent Hadoop Jobs in London

open-source ETL, and data pipeline orchestration tools such as Apache Airflow and Nifi. Experience with large scale/Big Data technologies, such as Hadoop, Spark, Hive, Impala, PrestoDb, Kafka. Experience with workflow orchestration tools like Apache Airflow. Experience with containerisation using Docker and deployment on Kubernetes. Experience with More ❯

S3, BigQuery, Redshift, Data Lakes). Expertise in SQL for querying large datasets and optimizing performance. Experience working with big data technologies such as Hadoop, Apache Spark, and other distributed computing frameworks. Solid understanding of machine learning algorithms, data preprocessing, model tuning, and evaluation. Experience in working with LLM More ❯

S3, BigQuery, Redshift, Data Lakes). Expertise in SQL for querying large datasets and optimizing performance. Experience working with big data technologies such as Hadoop, Apache Spark, and other distributed computing frameworks. Solid understanding of machine learning algorithms, data preprocessing, model tuning, and evaluation. Experience in working with LLM More ❯

models and platform products in general. Knowledge of time series analysis and forecasting techniques. Technical Skills: Languages: Python, SQL, R. Big Data: Apache Spark, Hadoop, Kafka, or similar. Timeseries Database: InfluxDB or TimescaleDB, or similar. Cloud Platforms: AWS Redshift, Azure Synapse, or similar. ML/AI Tools: scikit-learn More ❯

Data Analytics - Specialty or AWS Certified Solutions Architect - Associate. Experience with Airflow for workflow orchestration. Exposure to big data frameworks such as Apache Spark, Hadoop, or Presto. Hands-on experience with machine learning pipelines and AI/ML data engineering on AWS. Benefits: Competitive salary and performance-based bonus More ❯

a team-oriented environment. Preferred Skills: Experience with programming languages such as Python or R for data analysis. Knowledge of big data technologies (e.g., Hadoop, Spark) and data warehousing concepts. Familiarity with cloud data platforms (e.g., Azure, AWS, Google Cloud) is a plus. Certification in BI tools, SQL, or More ❯

large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale More ❯

large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale More ❯

Python and R, and ML libraries (TensorFlow, PyTorch, scikit-learn). Hands-on experience with cloud platforms (Azure ML) and big data ecosystems (e.g., Hadoop, Spark). Strong understanding of CI/CD pipelines, DevOps practices, and infrastructure automation. Familiarity with database systems (SQL Server, Snowflake) and API integrations. More ❯

proficiency in data modeling, SQL, NoSQL databases, and data warehousing. Hands-on experience with data pipeline development, ETL processes, and big data technologies (e.g., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Google Cloud, and cloud-based data services (e.g., AWS Redshift, Azure Synapse More ❯

Frameworks: Extensive experience with AI frameworks and libraries, including TensorFlow, PyTorch, or similar. ? Data Processing: Expertise in big data technologies such as Apache Spark, Hadoop, and experience with data pipeline tools like Apache Airflow. ? Cloud Platforms: Strong experience with cloud services, particularly AWS, Azure, or Google Cloud Platform, including More ❯

with programming languages such as Python or Java Understanding of data warehousing concepts and data modeling techniques Experience working with big data technologies (e.g., Hadoop, Spark) is an advantage Excellent problem-solving and analytical skills Strong communication and collaboration skills Benefits Enhanced leave - 38 days inclusive of 8 UK More ❯

with programming languages such as Python or Java. Understanding of data warehousing concepts and data modeling techniques. Experience working with big data technologies (e.g., Hadoop, Spark) is an advantage. Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Benefits Enhanced leave - 38 days inclusive of 8 UK More ❯

as MLflow, Kubeflow, TensorFlow Serving, and Seldon for seamless production workflows. Big Data Engineering : Architect high-performance data pipelines using Apache Spark, Kafka ,and Hadoop for real-time and batch processing. Generative AI : Explore and integrate generative technologies, including GPT, DALL-E , and GANs , for innovative applications in user More ❯

MongoDB, Cassandra). • In-depth knowledge of data warehousing concepts and tools (e.g., Redshift, Snowflake, Google BigQuery). • Experience with big data platforms (e.g., Hadoop, Spark, Kafka). • Familiarity with cloud-based data platforms and services (e.g., AWS, Azure, Google Cloud). • Expertise in ETL tools and processes (e.g. More ❯

large datasets. Expertise in creating impactful visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI. Familiarity with big data tools and platforms like Hadoop, Spark, or cloud-based tools (e.g., AWS, Google Cloud, Azure). Strong analytical skills with the ability to identify, define, and solve complex business More ❯

data modeling, and ETL/ELT processes.* Proficiency in programming languages such as Python, Java, or Scala.* Experience with big data technologies such as Hadoop, Spark, and Kafka.* Familiarity with cloud platforms like AWS, Azure, or Google Cloud.* Excellent problem-solving skills and the ability to think strategically.* Strong More ❯

Proven experience as a Data Engineer with a strong background in data pipelines. Proficiency in Python, Java, or Scala, and big data technologies (e.g., Hadoop, Spark, Kafka). Experience with Databricks, Azure AI Services, and cloud platforms (AWS, Google Cloud, Azure). Solid understanding of SQL and NoSQL databases. More ❯

Python, Golang, PowerShell, Ruby 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience Experience with big data technologies such as: Hadoop, Hive, Spark, EMR Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions Our inclusive culture More ❯

CloudSQL, Spanner, Firebase). Experience with data technologies: SQL, NoSQL, Data Warehousing, Data Lakes, and Data Lakehouse architectures with knowledge of Big Data technologies: Hadoop ecosystem, Spark or Kafka. Programming skills in Python or Java, with experience in data processing libraries. Experience designing and implementing big data solutions and More ❯

within all areas of responsibility and in particularly in Data & Analytics function. Expert proficiency in Python, R, SQL, and distributed computing frameworks (e.g., Spark, Hadoop). Advanced knowledge of data engineering tools (e.g., Airflow, Kafka, Snowflake, Databricks). Proficiency in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn). Ability More ❯

Proven experience as a Data Engineer with a strong background in data pipelines. Proficiency in Python, Java, or Scala, and big data technologies (e.g., Hadoop, Spark, Kafka). Experience with Databricks, Azure AI Services, and cloud platforms (AWS, Google Cloud, Azure). Solid understanding of SQL and NoSQL databases. More ❯

with have expertise in some of the following: Python, SQL, Scala, and Java for data engineering. Strong experience with big data tools (Apache Spark, Hadoop, Databricks, Dask) and cloud platforms (AWS, Azure, GCP). Proficient in data modelling (relational, NoSQL, dimensional) and DevOps automation (Docker, Kubernetes, Terraform, CI/ More ❯

will have expertise in some of the following: Python, SQL, Scala, and Java for data engineering. Strong experience with big data tools (Apache Spark, Hadoop, Databricks, Dask) and cloud platforms (AWS, Azure, GCP). Proficient in data modelling (relational, NoSQL, dimensional) and DevOps automation (Docker, Kubernetes, Terraform, CI/ More ❯

Strong team leadership skills in a data engineering environment. Management experience: career development, delivery management & skills assessment. Skilled in designing and building Databricks/Hadoop (Cloudera/HortonWorks)/Spark data products. Skilled in owning, designing and implementing data pipelines ingesting enterprise levels of data volume. In-depth knowledge More ❯