1 to 25 of 108 Apache Spark Jobs in the UK excluding London

data-based insights, collaborating closely with stakeholders. Passionately discover hidden solutions in large datasets to enhance business outcomes. Design, develop, and maintain data processing pipelines using Cloudera technologies, including Apache Hadoop, Apache Spark, Apache Hive, and Python. Collaborate with data engineers and scientists to translate data requirements into technical specifications. Develop and maintain frameworks for efficient More ❯

focused data team responsible for building and optimising scalable, production-grade data pipelines and infrastructure. Key Responsibilities: Design and implement robust, scalable ETL/ELT pipelines using Databricks and Apache Spark Ingest, transform, and manage large volumes of data from diverse sources Collaborate with analysts, data scientists, and business stakeholders to deliver clean, accessible datasets Ensure high performance … practices Work with cloud-native tools and services (preferably Azure ) Required Skills & Experience: Proven experience as a Data Engineer on cloud-based projects Strong hands-on skills with Databricks , Apache Spark , and Python or Scala Proficient in SQL and working with large-scale data environments Experience with Delta Lake , Azure Data Lake , or similar technologies Familiarity with version More ❯

focused data team responsible for building and optimising scalable, production-grade data pipelines and infrastructure. Key Responsibilities: Design and implement robust, scalable ETL/ELT pipelines using Databricks and Apache Spark Ingest, transform, and manage large volumes of data from diverse sources Collaborate with analysts, data scientists, and business stakeholders to deliver clean, accessible datasets Ensure high performance … practices Work with cloud-native tools and services (preferably Azure ) Required Skills & Experience: Proven experience as a Data Engineer on cloud-based projects Strong hands-on skills with Databricks , Apache Spark , and Python or Scala Proficient in SQL and working with large-scale data environments Experience with Delta Lake , Azure Data Lake , or similar technologies Familiarity with version More ❯

issues for non-technical audiences. Self-motivated and able to work independently. Preferred Qualifications: Background in investment banking or financial services. Hands-on experience with Hive, Impala, and the Spark ecosystem (e.g., HDFS, Apache Spark, Spark-SQL, UDFs, Sqoop). Proven experience building and optimizing big data pipelines, architectures, and data sets. More ❯

Criteria 5+ years of experience in Technical Data Analysis. Proficiency in SQL, Python, and Spark. Experience within an investment banking or financial services environment. Exposure to Hive, Impala, and Spark ecosystem technologies (e.g. HDFS, Apache Spark, Spark-SQL, UDF, Sqoop). Experience building and optimizing Big Data pipelines, architectures, and data sets. Familiarity with Hadoop and More ❯

lead Support the Ordnance Survey Testing Community, with common standards such as metrics and use of test tools Here is a snapshot of the technologies that we use Scala, Apache Spark, Databricks, Apache Parquet, YAML, Azure Cloud Platform, Azure DevOps (Test plans, Backlogs, Pipelines), GIT, GeoJSON What we're looking for Highly skilled in creating, maintaining and More ❯

years of experience in data engineering or a related field, with a focus on building scalable data systems and platforms. Expertise in modern data tools and frameworks such as Spark, dbt, Airflow, Kafka, Databricks, and cloud-native services (AWS, GCP, or Azure) Understanding of data modeling, distributed systems, ETL/ELT pipelines, and streaming architectures Proficiency in SQL and More ❯

years of experience in data engineering or a related field, with a focus on building scalable data systems and platforms. Expertise in modern data tools and frameworks such as Spark, dbt, Airflow, Kafka, Databricks, and cloud-native services (AWS, GCP, or Azure) Understanding of data modeling, distributed systems, ETL/ELT pipelines, and streaming architectures Proficiency in SQL and More ❯

of experience in data engineering or a related field, with a focus on building scalable data systems and platforms. Strong expertise with modern data tools and frameworks such as Spark , dbt , Airflow , Kafka , Databricks , and cloud-native services (AWS, GCP, or Azure). Deep understanding of data modeling , distributed systems , streaming architectures , and ETL/ELT pipelines . Proficiency More ❯

in data engineering, architecture, or platform management 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 and implementation. Hands More ❯

able to work across full data cycle. • Proven Experience working with AWS data technologies (S3, Redshift, Glue, Lambda, Lake formation, Cloud Formation), GitHub, CI/CD • Coding experience in Apache Spark, Iceberg or Python (Pandas) • Experience in change and release management. • Experience in Database Warehouse design and data modelling • Experience managing Data Migration projects. • Cloud data platform development … the AWS services like Redshift, Lambda,S3,Step Functions, Batch, Cloud formation, Lake Formation, Code Build, CI/CD, GitHub, IAM, SQS, SNS, Aurora DB • Good experience with DBT, Apache Iceberg, Docker, Microsoft BI stack (nice to have) • Experience in data warehouse design (Kimball and lake house, medallion and data vault) is a definite preference as is knowledge of … other data tools and programming languages such as Python & Spark and Strong SQL experience. • Experience is building Data lake and building CI/CD data pipelines • A candidate is expected to understand and can demonstrate experience across the delivery lifecycle and understand both Agile and Waterfall methods and when to apply these. Experience: This position requires several years of More ❯

management and associated tools such as Git/Bitbucket. Experience in the use of CI/CD tools such as Jenkins or an understanding of their role. Experience with Apache Spark or Hadoop. Experience in building data pipelines. Experience of designing warehouses, ETL pipelines and data modelling. Good knowledge in designing, building, using, and maintaining REST APIs. Good More ❯

tools (Git, Maven, Jenkins, CI/CD) Excellent communication and teamwork skills Bonus Points For Experience in payments, financial services, or fraud detection Familiarity with Big Data tools (Hadoop, Spark, Kafka) Exposure to cloud-native architecture (AWS, GCP, Azure) Understanding of TDD/BDD and modern testing frameworks What's On Offer Hybrid Working - Flexibility with 2 remote days More ❯

systems Clear communicator, able to translate 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 AI/LLM tools More ❯

systems Clear communicator, able to translate 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 AI/LLM tools More ❯

Driven Projects: Collaborate on exciting and complex data pipelines, platforms, and automation solutions across industries including finance, retail, and government. Cutting-Edge Tech: Work hands-on with tools like Spark, Databricks, Azure Deequ, and more. Career Development: We invest in your growth with dedicated training, mentoring, and support for certifications. Collaborative Culture: Join a diverse, inclusive team that thrives … you'll do: Maintain test automation frameworks tailored for data-intensive environments. Implement validation tests for data pipelines, data quality, and data transformation logic. Use tools like Azure Deequ , Spark , and Databricks to ensure data accuracy and completeness. Write robust, scalable test scripts in Scala , Python , and Java . Integrate testing into CI/CD pipelines and support infrastructure … and data validation techniques. Experience using test automation frameworks for data pipelines and ETL workflows Strong communication and stakeholder management skills. Nice-to-Have: Hands-on experience with Databricks , Apache Spark , and Azure Deequ . Familiarity with Big Data tools and distributed data processing. Experience with data observability and data quality monitoring. Proficiency with CI/CD tools More ❯

in Computer Science, Data Science, Engineering, or a related field. Strong programming skills in languages such as Python, SQL, or Java. Familiarity with data processing frameworks and tools (e.g., Apache Spark, Hadoop, Kafka) is a plus. Basic understanding of cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services. Knowledge of database systems (e.g., MySQL, PostgreSQL, MongoDB More ❯

Catalog). Familiarity with Data Mesh, Data Fabric, and product-led data strategies. Expertise in cloud platforms (AWS, Azure, GCP, Snowflake). Technical Skills Proficiency in big data tools (Apache Spark, Hadoop). Programming knowledge (Python, R, Java) is a plus. Understanding of ETL/ELT, SQL, NoSQL, and data visualisation tools. Awareness of ML/AI integration More ❯

knowledge of Kafka , Confluent , and event-driven architecture Hands-on experience with Databricks , Unity Catalog , and Lakehouse architectures Strong architectural understanding across AWS, Azure, GCP , and Snowflake Familiarity with Apache Spark, SQL/NoSQL databases, and programming (Python, R, Java) Knowledge of data visualisation, DevOps principles, and ML/AI integration into data architectures Strong grasp of data More ❯

Strong experience in Infrastructure 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 More ❯

Strong experience in Infrastructure 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 More ❯

Strong experience in Infrastructure 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 More ❯

systems such as: Ansible, Terraform, Jenkins, Bamboo, Concourse etc. Monitoring utilising products such as: Prometheus, Grafana, ELK, filebeat etc. Observability - SRE Big Data solutions (ecosystems) and technologies such as: Apache Spark and the Hadoop Ecosystem Edge technologies e.g. NGINX, HAProxy etc. Excellent knowledge of YAML or similar languages The following Technical Skills & Experience would be desirable for Data More ❯

their growth and development Apply agile methodologies (Scrum, pair programming, etc.) to deliver value iteratively Essential Skills & Experience Extensive hands-on experience with programming languages such as Python, Scala, Spark, and SQL Strong background in building and maintaining data pipelines and infrastructure In-depth knowledge of cloud platforms and native cloud services (e.g., AWS, Azure, or GCP) Familiarity with More ❯

experience working as a Software Engineer on large software applications Proficient in many of the following technologies - Python, REST, PyTorch, TensorFlow, Docker, FastAPI, Selenium, React, TypeScript, Redux, GraphQL, Kafka, Apache Spark. Experience working with one or more of the following database systems - DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing and tools - JUnit, Mockito, PyTest, Selenium. Strong working knowledge More ❯