26 to 50 of 317 Apache Spark Jobs in London

semi-structured, and unstructured dataHands-on expertise in containerization and orchestration using Docker and Kubernetes for scalable production systemsFamiliarity with distributed computing frameworks (e.g., Spark, Dask), cloud platforms (e.g., AWS, Azure, GCP), and analytics libraries (e.g., pandas, numpy, matplotlib)Exposure to DevOps, DataOps, and MLOps concepts and best practicesExperience ...

building, and operating ETL/ELT pipelines and large-scale data architectures.Hands-on experience with modern data stacks — our tech includes Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark, deployed via AWS, Docker, and Terraform (experience with similar technologies is essential).Ability ...

operating ETL/ELT pipelines and large-scale data architectures. Hands-on experience with modern data stacks — our tech includes Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark, deployed via AWS, Docker, and Terraform (experience with similar technologies is essential).Ability ...

high-growth environments.Technical Skills:Experience building and operating ETL/ELT pipelines.Hands-on experience with modern data stacks — our tech includes Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark, deployed via AWS, Docker, and Terraform (experience with some of these or similar ...

environments. Technical Skills: Experience building and operating ETL/ELT pipelines. Hands-on experience with modern data stacks — our tech includes Python, SQL, Snowflake, Apache Iceberg, AWS S3, PostgresDB, Airflow, dbt, and Apache Spark, deployed via AWS, Docker, and Terraform (experience with some of these or similar ...

Flask, Tornado 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 ...

within either Flask, Tornado or Django, DockerExperience working with ETL pipelines is desirable e.g. Luigi, Airflow or ArgoExperience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc. Data acquisition and development of data sets and improving data qualityPreparing data for predictive and prescriptive modellingHands on coding ...

standards. Skills Strong experience in SQL and data modeling concepts. Hands-on experience with ETL/ELT development. Proficiency in Python, Scala, or Spark-based data processing. Experience with cloud platforms such as AWS, Azure, or GCP. Knowledge of Data Warehouse and Data Lake architectures. Experience with orchestration tools ...

plusAbility to write clean, maintainable, scalable, and robust code in PythonFamiliarity with analytics libraries (e.g., pandas, numpy, matplotlib), distributed computing frameworks (e.g., Spark, Dask), and cloud platforms (e.g., AWS, Azure, GCP)Basic understanding or exposure to containerization technologies such as Docker and Kubernetes would be beneficialExposure to software engineering ...

strategic planningRequired Skills & ExperienceTechnical SkillsStrong proficiency in SQL and data modellingExcellent programming skills in Python, Scala, or JavaHands-on experience with distributed data frameworks (Spark, Hadoop)Experience with data pipelines and ETL toolsExpertise in at least one cloud platform:Azure (Synapse, Data Factory, Databricks)AWS (S3, Glue, Redshift, Lambda ...

continuous learning to understand new data management systems Strong proficiency in hands-on development using industry-standard tools and languages (e.g., SQL, Python, Spark, cloud platforms), with a commitment to code quality and delivery excellence Preferred qualifications, capabilities, and skills Hands-on experience with Databricks for scalable data engineering … analytics, and collaborative development, as well as proficiency in managing large datasets using Apache Iceberg for efficient, reliable table management and data lake optimization Experience architecting and implementing enterprise-scale data warehouses on cloud platforms (e.g., AWS, Azure, GCP), including migration from legacy systems Advanced proficiency in designing ...

continuous learning to understand new data management systems Strong proficiency in hands-on development using industry-standard tools and languages (e.g., SQL, Python, Spark, cloud platforms), with a commitment to code quality and delivery excellence Preferred qualifications, capabilities, and skills Hands-on experience with Databricks for scalable data engineering … analytics, and collaborative development, as well as proficiency in managing large datasets using Apache Iceberg for efficient, reliable table management and data lake optimization Experience architecting and implementing enterprise-scale data warehouses on cloud platforms (e.g., AWS, Azure, GCP), including migration from legacy systems Advanced proficiency in designing ...

engineers should be adept at Extract, Transform, Load (ETL) processes. This includes using frameworks such as the Medallion Architecture, as well as tools like Apache Airflow, Fivetran, Azure Data Factory, and Amazon Glue to automate data workflows and ensure data quality. Familiarity with cloud computing services, such … Cloud Platform, is increasingly important. Data engineers often leverage these platforms for scalable data storage and processing capabilities. Knowledge of big data frameworks like Apache Spark, Beam is crucial for handling large datasets and real-time data processing. Understanding data modelling techniques helps data engineers design efficient data ...

practitioner, you will collaborate with architects, engineers, and analysts to deliver secure, scalable, and high-performing migration solutions, leveraging technologies such as AWS Glue, Apache Iceberg, Python/PySpark, SQL, and YAML-based configurations.You will thrive in a collaborative, client-facing environment, with a passion for solving complex data … target cloud architecturesData Pipeline Testing & Validation (Core Focus)Test end-to-end ETL data solutions running on AWS services, including AWS Glue and Apache IcebergValidate data feed pipeline migrations transitioning to new AWS Data Lakehouse architecturesTest pipelines underpinned by:AI ETL acceleratorsPython/PySpark transformationsSQL-based logicYAML configuration-driven ...

Flask, Tornado 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 Reporting ...

platformsAdvanced Python and SQL skills, with experience in modern engineering practices such as CI/CD and TDDExperience with big data frameworks such as Apache Spark or similar technologiesSolid understanding of data modelling, data warehousing, OLTP and data lake architecturesExperience working with cloud ecosystems such as AWS, Azure ...

complex stakeholder environments. Strong understanding of data pipelines, ETL/ELT frameworks, orchestration tooling, and data engineering best practices. Familiarity with technologies such as Spark, Python, SQL, Delta Lake, Unity Catalog, dbt, Azure Data Factory, Airflow, Kafka, or equivalent tooling. Relevant certifications such as Snowflake SnowPro, Databricks Certified Data ...

modern frameworksWorking knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models ...

modern frameworksWorking knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models ...

modern frameworksWorking knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models ...

measurable value.• Excellent analytical, problem-solving, and communication skills.• Proven experience leading data modernisation initiatives across multiple sectors.Technical Expertise• Proficiency with big data technologies: Apache Spark, Kafka, Confluent, Databricks, Unity Catalog.• Strong knowledge of cloud ecosystems (AWS, Azure, GCP) and infrastructure-as-code (Terraform, CloudFormation).• Hands ...

data quality and consistency across platforms while complying with governance policies.What You Bring:Core Development Skills:- Proficient in Python and SQL, with experience in Apache Spark.- Strong experience building backend services and APIs.Database Expertise:- Solid understanding of relational databases (SQL Server, PostgreSQL) and distributed NoSQL databases (Cassandra, MongoDB, Cosmos ...

quality and consistency across platforms while complying with governance policies. What You Bring: Core Development Skills:- Proficient in Python and SQL, with experience in Apache Spark.- Strong experience building backend services and APIs. Database Expertise:- Solid understanding of relational databases (SQL Server, PostgreSQL) and distributed NoSQL databases (Cassandra, MongoDB ...

Azure, GCP).Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon).Familiarity with data lake architectures and distributed processing frameworks (e.g., Spark, Hadoop).Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines.Understanding of data governance, security, and compliance frameworks.Exposure to AI/ML workloads ...

Azure) Experience with: o Data warehousing and lake architectures o ETL/ELT pipeline development o SQL and NoSQL databases o Distributed computing frameworks (Spark, Kinesis etc) o Software development best practices including CI/CD, TDD and version control. o Containerisation tools like Docker or Kubernetes o Experience ...