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 ❯
Skills: Advanced proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL). Big Data Technologies: Extensive experience with big data technologies (e.g., Hadoop, Spark). Cloud Platforms: Deep understanding of cloud platforms (AWS, GCP, Azure) and their data services. DevOps Expertise: Strong understanding and practical 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 ❯
london, south east england, united kingdom Hybrid / WFH Options
Careerwise
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 ❯
years of relevant experience. Strong Knowledge of SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience with Big Data Technologies such as Hadoop, Spark, and Kafka. Proficiency in Programming Languages like Python, Java, or Scala. Familiarity with Cloud Platforms (AWS, Azure, or Google Cloud) and their data services. 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 similar role, with a focus on data infrastructure management Proficiency in data technologies, such as relational databases, data warehousing, big data platforms (e.g., Hadoop, Spark), data streaming (e.g., Kafka), and cloud services (e.g., AWS, GCP, Azure). Ideally some programming skills in languages like Python, Java, or Scala 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 ApacheHadoop 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 ApacheHadoop 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 ❯
Computer Science , Information Technology, or equivalent experience, coupled with relevant professional certifications . Advanced SQL knowledge for database querying. Proficiency with big data tools (Hadoop, Spark) and familiarity with big data file formats (Parquet, Avro). Skilled in data pipeline and workflow management tools (Apache Airflow, NiFi). Strong More ❯
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 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 ❯
roles, with 5+ years in leadership positions. Expertise in modern data platforms (e.g., Azure, AWS, Google Cloud) and big data technologies (e.g., Spark, Kafka, Hadoop). Strong knowledge of data governance frameworks, regulatory compliance (e.g., GDPR, CCPA), and data security best practices. Proven experience in enterprise-level architecture design 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 ❯
You Have: Experience with a public cloud, including AWS, Microsoft Azure, or Google Cloud Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka Experience working on real-time data and streaming applications Experience with UNIX and Linux, including basic commands and Shell More ❯
Shell scripting Experience with a public cloud, including AWS, Microsoft Azure, or Google Cloud Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka Experience working on real-time data and streaming applications Experience with NoSQL implementation, including MongoDB or Cassandra Experience with More ❯
TS/SCI clearance Master's degree Nice If You Have: 5+ years of experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka 5+ years of experience working on real-time data and streaming applications 5+ years of experience with NoSQL implementation More ❯
with AI infrastructure, application deployment, and operations. Strong knowledge of containerization technologies such as Docker and Kubernetes. Strong experience with big data technologies (e.g., Hadoop, Spark) and their applications in AI. Experience with tools like Kubernetes and Git. Experience with analytics and programming tools, including Python and R. Experience More ❯
with data visualisation tools such as Tableau, Power BI, or visualisation libraries in Python (matplotlib, Seaborn). Experience with big data technologies such as Hadoop, Spark, or distributed computing frameworks. Strong understanding of data preprocessing, feature engineering, and data pipeline development. Bachelor's or Master's degree in Data 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 ❯
modern data architectures, ensuring scalability and performance. Work on data integration, ETL, warehousing, and API development. Deploy and maintain big data technologies such as Hadoop, Spark, and Kafka. Develop data models and visualisation solutions using Power BI, Tableau, or Looker. Ensure compliance with GDPR, CCPA, and data governance policies. More ❯
