26 to 50 of 553 Apache Spark Jobs in the UK

data strategy Helping build and mentor the future data engineering team as the company scales Python, PostgreSQL, MongoDB, vector databases (Qdrant, Milvus or pgvector), Apache Spark, Apache Airflow, Kafka, Elasticsearch/OpenSearch, Pandas, Polars. What they're looking for: 7+ years of experience in data engineering ...

frameworks, tools and applications that make that data available to other teams and systems. Your primary toolset in your work will be Java microservices, Spark/Scala data processing (also some Flink), Kubernetes and AWS native tooling.**What You'll Do:*** Write highly fault-tolerant Java code within Apache Spark to produce platform products used by our customers to query our event pipelines/ingestion for insight into active threat trends and related analytics* Design, develop, and maintain ultra-high-scale data platforms that process petabytes of data* Participate in technical reviews of our products and help ...

frameworks, tools and applications that make that data available to other teams and systems. Your primary toolset in your work will be Java microservices, Spark/Scala data processing (also some Flink), Kubernetes and AWS native tooling. What You'll Do: Write highly fault-tolerant Java code within Apache Spark to produce platform products used by our customers to query our event pipelines/ingestion for insight into active threat trends and related analyticsDesign, develop, and maintain ultra-high-scale data platforms that process petabytes of dataParticipate in technical reviews of our products and help us develop ...

knowledge of optimisation techniques for large‐scale data processing Strong proficiency in SQL and Python for handling complex data problems Hands‐on experience with Apache Spark (PySpark or Spark SQL) Experience with the Azure data stack Knowledge of workflow orchestration tools like Azure Data Factory or Apache … cost optimisation strategies for cloud data platforms Experience with data quality frameworks and implementation Experience with data visualisation tools like Power BI or Apache Superset Experience with other cloud data platforms like AWS, GCP or Oracle Experience with modern unified data platforms like Databricks or Microsoft Fabric Experience with ...

DevOps knowledge, including Linux/Unix, OpenShift/Kubernetes, and Ansible. Experience building and supporting reactive, event-sourced systems. Hands-on experience with Apache Spark and Apache Flink. Strong understanding of Apache HTTP, Envoy, and REST API design. Experience working with Protobuf, Avro, and Parquet data ...

Modern data platforms: Databricks, Snowflake and cloud‐native data services Data architecture: Lakehouse, medallion, data mesh and domain‐oriented patterns Data processing: Apache Spark, SQL and distributed processing technologies Data storage: Delta Lake, Apache Iceberg and open table formats Data pipelines: Enterprise‐scale ETL/ELT, batch … streaming patterns Transformation and quality: dbt, Great Expectations and equivalent tooling Orchestration: Apache Airflow and cloud‐native orchestration services Event streaming: Kafka and cloud‐native event‐streaming technologies Data modelling: Dimensional, analytical and domain‐oriented modelling Data governance: Catalogue, metadata, lineage, quality, security, privacy and access controls Data products ...

data processing solutions using BigQuery, Pub/Sub, Dataflow and Dataproc Experience developing end‐to‐end solutions using batch and streaming frameworks such as Apache Spark and Apache Beam. Expert understanding of when to use a range of data storage technologies including relational/non‐relational, document ...

Modern data platforms: Databricks, Snowflake and cloud-native data services Data architecture: Lakehouse, medallion, data mesh and domain-oriented patterns Data processing: Apache Spark, SQL and distributed processing technologies Data storage: Delta Lake, Apache Iceberg and open table formats Data pipelines: Enterprise-scale ETL/ELT, batch … streaming patterns Transformation and quality: dbt, Great Expectations and equivalent tooling Orchestration: Apache Airflow and cloud-native orchestration services Event streaming: Kafka and cloud-native event-streaming technologies Data modelling: Dimensional, analytical and domain-oriented modelling Data governance: Catalogue, metadata, lineage, quality, security, privacy and access controls Data products ...

ready data processing solutions using BigQuery, Pub/Sub, Dataflow and DataprocExperience developing end-to-end solutions using batch and streaming frameworks such as Apache Spark and Apache Beam. Expert understanding of when to use a range of data storage technologies including relational/non-relational, document ...

Document stores).Hands-on experience building, scaling, and shipping large-scale data platforms in production. Deep practical experience with distributed data processing frameworks (e.g., Apache Spark, Flink, Hadoop).Strong expertise in message brokers and event streaming platforms (e.g., Apache Kafka, Kinesis).End-to-end exposure to data … pipeline lifecycle development, including extensive experience with workflow orchestration tools (e.g., Apache Airflow, Dagster).Hands-on expertise with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and data lake architectures (e.g., Databricks, Delta Lake, Apache Iceberg).Advanced SQL skills and proficiency in Python. Strong background in modern software development ...

Solid knowledge of Linux/Unix, OpenShift/Kubernetes, and Ansible. Distributed Systems: Experience building and supporting reactive, event-sourced systems, with hands-on Apache Spark and Apache Flink. APIs & Data Formats: Strong understanding of Apache HTTP, Envoy, and REST API design, plus experience with Protobuf ...

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 ...

ingestion, incremental loading, and change data capture. Design and implement layered data models and transformation logic (raw, conformed, aggregated) using tools such as dbt, Spark, and cloud‐native processing services, ensuring data is clean, consistent, and ready for downstream consumption. Develop modular, reusable data transformation components and functions that … tooling, or cloud cost management across at least one major cloud provider (Azure, AWS, or GCP). Technical Skills Data engineering: strong proficiency with Apache Spark, dbt, Airflow/Azure Data Factory or equivalent orchestration, Data Lake/Apache Iceberg, and advanced SQL for large‐scale transformations. ...

wider stakeholders. Technical Environment Experience with some of the following technologies would be beneficial: Python SQL Cloud platforms – AWS, Azure or GCP Databricks Apache Spark/PySpark Kafka/event-driven architectures Airflow dbt Git/GitHub CI/CD Infrastructure as Code Modern data platforms and distributed ...

where needed. Qualification Core Data Engineering Strong programming proficiency in Java (preferred) or Python. Hands‐on experience with at least one of: Kafka, Flink, Spark (Flink/Kafka preferred for streaming). Solid understanding of stream processing concepts (e.g., event time, state, backpressure). Understanding of software engineering best ...

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 ...

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 ...

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 ...

across the team and support the continued growth of Unifeye. What You'll Do Design and build scalable batch and streaming data pipelines using Spark and Databricks. Develop modern Lakehouse architectures using Delta Lake and Medallion (Bronze, Silver and Gold) design principles. Build robust ingestion frameworks from APIs, files … pipelines, deployment automation and infrastructure-as-code practices. What We're Looking For Essential Strong commercial experience building production-grade data pipelines using Apache Spark (PySpark preferred). Experience delivering modern data platforms using Databricks or an equivalent Spark-based platform. Strong SQL skills and experience with ...

platforms:AWS,Azureand GCP Modern data platforms:Databricks, Snowflake and cloud-native data services Data architecture:Lakehouse, medallion, datameshand domain-oriented patterns Data processing:Apache Spark,SQLand distributed processing technologies Data storage:Delta Lake, ApacheIcebergand open table formats Data pipelines:Enterprise-scale ETL/ELT,batchand streaming patterns … Transformation and quality:dbt, Great Expectations and equivalent tooling Orchestration:Apache Airflow and cloud-native orchestration services Event streaming:Kafka and cloud-native event-streaming technologies Data modelling:Dimensional,analyticaland domain-oriented modelling Data governance:Catalogue, metadata, lineage, quality, security,privacyand access controls Data products:Reusable, discoverable and well ...

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 ...

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 ...

delivery across a range of projects, including data analysis, extraction, transformation, and loading, data intelligence, data security and proven experience in their technologies (e.g. Spark, cloud-based ETL services, Python, Kafka, SQL, Airflow) You have experience in assessing the relevant data quality issues based on data sources & uses cases ...

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 ...

common technical standards and ways of working, and presenting these to the community Here's a snapshot of the technologies that we use: Scala Apache Spark Databricks Apache Parquet Azure Cloud Platform Azure DevOps (Test plans, Backlogs, Pipelines) GIT What we're looking for We'd love ...