1 to 25 of 88 Apache Spark Jobs in London

Lead Backend Software Engineer – Data Services Federated Data Services | Distributed Systems | Apache Spark | DataFusion | Python | Kubernetes Location: London (2 days per week onsite) Rate: £700–£800 per day (Outside IR35) Duration: Initial 6-month contract with likely extension Overview We're looking for an experienced Lead Backend Software … data services capable of securely querying and processing data across multiple environments. Design high-performance data processing and streaming workflows using technologies such as Apache Spark, Apache DataFusion, or similar distributed query frameworks. Design and implement Kubernetes-based services and containerised workloads. Build secure APIs and backend ...

Highly competitive Status: Inside IR35 Working pattern: Full time We are seeking an experienced Technical Delivery Lead with strong expertise in Python, Databricks, and Apache Spark to lead the successful delivery of enterprise-scale data engineering initiatives.. This is a hands-on leadership role combining technical delivery, solution … Technical Delivery Lead position. Strong hands-on expertise with Python. Extensive experience with Databricks, including Workflows, Notebooks, and Delta Lake. Strong experience with Apache Spark/PySpark. Proven track record building and optimising large-scale ETL/ELT pipelines. Strong understanding of modern data architecture and Lakehouse principles. ...

designing and delivering modern data platforms, working closely with clients and technical teams to build scalable, secure and high-performing solutions using Databricks, Apache Spark and cloud technologies. You'll also play a key role in pre-sales activities, solution design, and helping to drive strategic Databricks initiatives … Databricks and modern data platform delivery. Skills & Experience Required 7+ years' experience in Data Architecture or Solution Architecture. Strong commercial experience with Databricks and Apache Spark . Experience delivering enterprise data platforms on AWS, Azure or GCP. Strong understanding of data warehousing, data lakes and lakehouse architectures. Experience ...

data solutions. Key Responsibilities Design, develop, and maintain scalable data pipelines using Python and Databricks Build and optimise ETL/ELT workflows leveraging Databricks (Spark, Delta Lake) Develop robust data models and data architectures on Lakehouse platforms Implement and manage data workflows within the Databricks ecosystem Ensure high data … Experience Extensive experience in data engineering Strong programming expertise in Python Hands-on experience with Databricks (workflows, notebooks, jobs, Delta Lake) Strong experience with Apache Spark/PySpark Experience building scalable ETL pipelines Proficiency in SQL and relational databases Experience with cloud platforms (AWS/Azure/ ...

data engineering tasks. Experience with cloud platforms such as: AWS, Azure, Google Cloud Platform (GCP) Hands-on experience with data technologies such as: Apache Spark, Databricks, Kafka, Airflow, Snowflake, Hadoop ecosystems Experience designing and maintaining data warehouses and data lakes. Knowledge of DevOps and CI/CD tooling ...

domain-driven approaches CI/CD for data pipelines, version control discipline, infrastructure-as-code basics (Terraform) Streaming and big data exposure (Kafka, Kinesis, Spark, Flink) is a plus Data quality and observability tooling (Great Expectations, Monte Carlo, Soda, dbt tests) is a plus Engineering & Soft Skills: Strong software ...

Django, Docker \n Experience working with ETL pipelines is desirable e.g. Luigi, Airflow or Argo \n Experience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc. \n Data acquisition and development of data sets and improving data quality \n Preparing data for predictive and prescriptive modelling ...

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 ...

Flask, Tornado or Django, Docker\ NExperience working with ETL pipelines is desirable e.G. Luigi, Airflow or Argo\ NExperience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc.\ NData acquisition anddevelopment of data sets and improving data quality\ NPreparing data for predictive and prescriptive modelling\ NHands ...

Django, Docker \n Experience working with ETL pipelines is desirable e.g. Luigi, Airflow or Argo \n Experience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc. \n Data acquisition and development of data sets and improving data quality \n Preparing data for predictive and prescriptive modelling ...

Flask, Tornado or Django, Docker \ NExperience working with ETL pipelines is desirable e.G. Luigi, Airflow or Argo \ NExperience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc.\ NData acquisition anddevelopment of data sets and improving data quality \ NPreparing data for predictive and prescriptive modelling \ NReporting tools ...

consultancy or client delivery setting. Proven experience across data analysis, extraction, transformation, and loading, with hands-on knowledge of relevant technologies such as Spark, Python, Kafka, SQL, Airflow, and cloud-based ETL services. Experience designing schemas for data warehouses and data marts, assessing data quality issues, and integrating quality ...

production environments Familiarity with Azure Machine Learning and AI services Exposure to data visualisation tools (e.g. Power BI) Experience with big data frameworks (Spark, Kafka) Knowledge of data governance, lineage, and metadata tooling SHIFT & WORKING PATTERN Standard business hours, with participation in an on-call rota as required Occasional ...

considering: We are looking for an experienced AWS Databricks Engineer with strong hands-on expertise in Databricks, AWS cloud services, PySpark, Spark SQL, Delta Lake, Python, SQL, and data engineering. The candidate will be responsible for designing, developing, optimizing, and supporting scalable data pipelines and lakehouse solutions … time. Your Role: Design, develop, and maintain scalable data pipelines using Databricks on AWS. Build and optimize data processing frameworks using PySpark, Spark SQL, Python, Delta Lake, and Databricks notebooks. Develop end-to-end ETL/ELT pipelines for ingestion, transformation, validation, and consumption. Work with AWS services such ...

monitoring Strong background in CI/CD pipeline design, automation, and maintenance Hands‐on experience with data engineering tools and storage solutions (e.g., ADLS, Spark, Databricks, SQL/NoSQL databases) Experience with distributed computing and big data processing frameworks such as PySpark Knowledge of infrastructure‐as‐code tools such ...

with proficiency in SQL. \n Experience building data platforms on cloud services like Databricks on Azure or GCP BigQuery and big data technologies like Spark, Flink etc. \n Solid grasp of Kubernetes, CI/CD and Terraform. \n Working knowledge of ETL and ELT frameworks and orchestration tools like ...

along with proficiency in SQL. Experience building data platforms on cloud services like Databricks on Azure or GCP BigQuery and big data technologies like Spark, Flink etc. Solid grasp of Kubernetes, CI/CD and Terraform. Working knowledge of ETL and ELT frameworks and orchestration tools like Airflow ...

data into something they can actually use and act on. What you'll be doing: Designing and building performant data ingestion pipelines using Apache Spark, ideally within Azure Databricks Contributing to the platform's data modelling and architecture Building dashboards and semantic models, and presenting these directly ...

data into something they can actually use and act on. What you'll be doing: Designing and building performant data ingestion pipelines using Apache Spark, ideally within Azure Databricks Contributing to the platform's data modelling and architecture Building dashboards and semantic models, and presenting these directly ...

skills and understanding of modern data engineering principles. Desirable Skills Python and PySpark development. AWS services including EMR, S3, Lambda, Athena, and Redshift. Apache Spark and large-scale batch processing. Terraform, CI/CD pipelines, and Infrastructure as Code. Git/GitLab and modern software development practices. ...

environments Owning the cloud infrastructure underpinning data systems through a DevOps approach Knowledge of handling and transforming various data types (JSON, CSV, etc) with Apache Spark, Databricks or Hadoop Good understanding of the possible architectures involved in modern data system design (e.g. Data Warehouse, Data Lakes and Data ...

houses and data mesh \n Strong understanding of best practice DataOps and MLOps \n Up-to-date understanding of various data engineering technologies including Apache Spark, Databricks and Hadoop \n Strong understanding of agile ways of working \n Up-to-date understanding of various programming languages including Python ...

data lake houses and data mesh Strong understanding of best practice DataOps and MLOps Up-to-date understanding of various data engineering technologies including Apache Spark, Databricks and Hadoop Strong understanding of agile ways of working Up-to-date understanding of various programming languages including Python, Scala ...

Exceptional programming skills in Python, with a command of data science libraries (e.g., Pandas, NumPy, Scikit‐learn). Experience with big data technologies (e.g., Spark, Trino, Hadoop) and ideally cloud platforms (e.g., AWS, GCP, Azure) for scalable data processing, storage, and model deployment. Ideally having understanding and practical experience ...

Exceptional programming skills in Python, with a command of data science libraries (e.g., Pandas, NumPy, Scikit‐learn). Experience with big data technologies (e.g., Spark, Trino, Hadoop) and ideally cloud platforms (e.g., AWS, GCP, Azure) for scalable data processing, storage, and model deployment. Ideally having understanding and practical experience ...