1 to 25 of 155 Remote Hadoop Jobs

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 ❯

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 ❯

problem-solving, and critical thinking skills. 8.Experience with social media analytics and understanding of user behaviour. 9.Familiarity with big data technologies, such as Apache Hadoop, Apache Spark, or Apache Kafka. 10.Knowledge of AWS machine learning services, such as Amazon SageMaker and Amazon Comprehend. 11.Experience with data governance and security 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 ❯

or 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 Hands on coding experience 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 ❯

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 ❯

government, defence, or highly regulated industries with knowledge of relevant standards Experience with additional data processing and ETL tools like Apache Kafka, Spark, or Hadoop Familiarity with containerization and orchestration tools such as Docker and Kubernetes Experience with monitoring and alerting tools such as Prometheus, Grafana, or ELK for More ❯

compliance (GDPR). Document data lineage and recommend improvements for data ownership and stewardship. Qualifications Programming: Python, SQL, Scala, Java. Big Data: Apache Spark, Hadoop, Databricks, Snowflake, etc. Cloud: AWS (Glue, Redshift), Azure (Synapse, Data Factory, Fabric), GCP (BigQuery, Dataflow). Data Modelling & Storage: Relational (PostgreSQL, SQL Server), NoSQL More ❯

complex data concepts to cross-functional teams Bonus points for experience with: DevOps tools like Docker, Kubernetes, CI/CD Big data tools (Spark, Hadoop), ETL workflows, or high-throughput data streams Genomic data formats and tools Cold and hot storage management, ZFS/RAID systems, or tape storage More ❯

validate solutions for quality assurance. Qualifications: Proven experience as a Data Engineer, especially with data pipelines. Proficiency in Python, Java, or Scala; experience with Hadoop, Spark, Kafka. Experience with Databricks, Azure AI Services, and cloud platforms (AWS, Google Cloud, Azure). Strong SQL and NoSQL database skills. Problem-solving More ❯

complex data concepts to cross-functional teams Bonus points for experience with: DevOps tools like Docker, Kubernetes, CI/CD Big data tools (Spark, Hadoop), ETL workflows, or high-throughput data streams Genomic data formats and tools Cold and hot storage management, ZFS/RAID systems, or tape storage 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 ❯

platforms (AWS, GCP, Azure) and cloud services for data storage, processing, and analytics. Nice-to-Have Requirements Familiarity with big data technologies such as Hadoop, Kafka, and similar tools. Familiarity with version control systems like Git. Experience with GraphDB, MongoDB, SQL/NoSQL, MS Access, and other databases. Ability More ❯

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 More ❯

features of cloud platforms (AWS, Azure, Google Cloud) such as machine learning services and automated data pipeline tools. Familiarity with big data frameworks like Hadoop or Spark is beneficial. Skills in advanced data visualization tools and software beyond basic reporting—such as Tableau, Power BI, or even more sophisticated More ❯

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 More ❯

lakehouse architectures. - Knowledge of DevOps practices, including CI/CD pipelines and version control (eg, Git). - Understanding of big data technologies (eg, Spark, Hadoop) is a plus. Seniority level Seniority level Mid-Senior level Employment type Employment type Contract Job function Job function Information Technology Referrals increase your More ❯

data modelling (relational, NoSQL) and ETL/ELT processes. Experience with data integration tools (e.g., Kafka, Talend) and APIs. Familiarity with big data technologies (Hadoop, Spark) and real-time streaming. Expertise in cloud security, data governance, and compliance (GDPR, HIPAA). Strong SQL skills and proficiency in at least More ❯

stakeholders and end users conveying technical concepts in a comprehensible manner Skills across the following data competencies: SQL (AWS Athena/Hive/Snowflake) Hadoop/EMR/Spark/Scala Data structures (tables, views, stored procedures) Data Modelling - star/snowflake Schemas, efficient storage, normalisation Data Transformation DevOps More ❯

and SQL.+ In-depth experience with data manipulation and visualization libraries (e.g., Pandas, NumPy, Matplotlib, etc.).+ Solid understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).+ Strong expertise in the full data science lifecycle: data collection, preprocessing, model development, deployment, and More ❯

computing frameworks such as Spark, KStreams, Kafka. Experience with Kafka and streaming frameworks. Understanding of monolithic vs. microservice architectures. Familiarity with Apache ecosystem including Hadoop modules (HDFS, YARN, HBase, Hive, Spark) and Apache NiFi. Experience with containerization and orchestration tools like Docker and Kubernetes. Knowledge of time-series or More ❯

Azure, GCP, Snowflake). Understanding of Data Mesh, Data Fabric, and product-led data strategies. Technical Knowledge: Familiarity with big data technologies (Apache Spark, Hadoop). Knowledge of programming languages such as Python, R, or Java. Experience with ETL/ELT processes, SQL, NoSQL databases, and DevOps principles. Understanding More ❯

the ability to convey complex concepts to non-technical stakeholders. Desirable (but not essential): Experience with SSIS, AWS or Azure Data Factory. Familiarity with Hadoop, Jenkins, or DevOps practices including CI/CD. Cloud certifications (Azure or AWS). Knowledge of additional programming languages or ETL tools. This is More ❯