26 to 50 of 249 PySpark Jobs in the UK

analytics libraries (e.g., pandas, numpy, matplotlib)Exposure to DevOps, DataOps, and MLOps concepts and best practicesExperience with core technologies such as Python, PySpark, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud services (AWS/GCP/Azure)Experience with Generative AI (GenAI) or agentic systems ...

deliver practical, value-driven data solutions. Drive continuous improvement across data engineering, reporting, and analytics capabilities. Essential Requirements Strong expertise in SQL and Python (PySpark desirable). Proven experience designing and building cloud-based data platforms from inception. Hands-on experience with Databricks, Spark, modern data lake/lakehouse ...

Databricks. Requirements Required Skills & Technologies Cloud Platforms: Hands‐on engineering experience within the AWS ecosystem (S3, IAM, Glue) and deep expertise in Databricks (SparkSQL, PySpark, governed cataloging). Modern Data Stack: Strong proficiency in dbt (Core or Cloud) for data transformation and understanding of modern table formats, specifically Apache ...

Databricks. Requirements Required Skills & Technologies Cloud Platforms: Hands-on engineering experience within the AWS ecosystem (S3, IAM, Glue) and deep expertise in Databricks (SparkSQL, PySpark, governed cataloging). Modern Data Stack: Strong proficiency in dbt (Core or Cloud) for data transformation and understanding of modern table formats, specifically Apache ...

best practices, including DevOps, DataOps, and MLOps, will be advantageousWhile we advocate using the right tech for the right task, we often leverage: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro (our open-source data pipelining framework), Dask/RAPIDS, Docker, Kubernetes, and cloud solutions such ...

need deep, hands‐on fluency across the modern Azure data stack — and strong bridging skills for the legacy environment you're migrating from. Python, PySpark, Azure Databricks (Delta Lake, Unity Catalog, pipelines, notebooks, jobs), Azure Data Lake Storage Gen2, Azure Data Factory, Azure Key Vault, Power BI (complex model ...

OneLake, pipelines and notebooks, and how these can be used to build scalable enterprise data solutions. Strong experience with SQL, Python and/or PySpark and modern data engineering approaches. Strong understanding of data architecture and data modelling, including designing reusable and scalable data structures. Experience implementing modern software ...

clear technical tasks. Mentorship: A proactive mindset, eager to provide guidance to less experienced developers and support collaborative pair-programming. Desirable Skills Experience with PySpark or PySQL. Experience in Dimensional Modelling and data warehousing principles. Experience with SQL Server SSAS Tabular and DAX. Exposure to SQL Server RDBMS ...

Skills Significant experience leading the design, development, and operation of enterprise‐scale data engineering platforms and teams. Strong technical expertise in SQL and Python, PySpark, or equivalent technologies for data ingestion, transformation, and validation. Experience with Microsoft Fabric or similar modern cloud‐based analytics and data platforms. Strong experience ...

Data Stack, critically ADF and Synapse (experience with Microsoft Fabric is a plus) Highly developed python and data pipeline development knowledge, must include substantial PySpark experience Demonstrable DevOps and DataOps experience with an understanding of best practices for engineering, test and ongoing service delivery An understanding of Infrastructure ...

analytics strategy AI & Automation Agentic AI patterns and intelligent automation solutions Document intelligence and AI-driven business use cases SQL Python and/or PySpark Data pipelines and transformation frameworks CI/CD concepts and deployment automation Azure DevOps and/or GitHub Infrastructure as Code awareness Environment promotion ...

Azure Synapse, Azure Data Factory, and Databricks. Knowledge of SSRS, SSAS, SSIS, DAX, or MDX. Experience with Azure DevOps and Git. Exposure to Databricks, PySpark, or advanced analytics technologies. Microsoft Data and AI certifications, particularly Microsoft Fabric certifications. What can we offer you? Healthcare cash plan. This will give ...

need to do the role:Expert-level experience in enterprise-scale data engineering, including defining standards, patterns, and overseeing complex delivery.Advanced proficiency in PySpark and SQL, with deep expertise in performance optimisation and scalable data processing.Strong experience in data modelling and large-scale data integration design, with the ability ...

Strong hands-on experience with Quantexa implementation and development. Proven experience building and supporting big data platforms . Advanced knowledge of Apache Spark/PySpark . Strong programming skills in Scala and/or Python . Expertise in SQL and large-scale data modelling. Strong cloud experience across ...

Looking ForProven hands-on experience with the Databricks platform, including orchestration and enterprise-scale Azure data engineering solutions.Strong technical skills in Python, PySpark, and SQL, with experience working with distributed data processing frameworks.Expertise delivering end-to-end data pipelines across ingestion, transformation, and serving layers within modern Data Lakehouse ...

data feed pipeline migrations repointing from an existing Data Warehouse to a new AWS Data Lakehouse architecture, underpinned with AI ETL accelerators, Python/PySpark, SQL and YAML configs.Test initial bulk then deltas from legacy to new.Execute automated test suites as part of sprint-based delivery and regression cyclesDefect ...

system through your contributions - big advantage.Familiarity with ClickHouse or similar high-performance data platforms.Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.CompensationFor roles based in the United States, the typical starting salary range for this position ...

such as S3, Kinesis, DMS, SNS, Glue, Athena, RDS • Experience with Data Warehouse databases like Snowflake/Redshift/BigQuery • Experience with Python/PySpark • Experience with implementing Rest APIs. • Experience with using and integrating BI tools such as QuickSight, Looker, Power BI, Tableau • Business Intelligence (BI) knowledge/ ...

pipelines. Experience working with cloud data platforms (AWS preferred). Exposure to data warehousing, data lakes and modern data architecture. Python experience (including PySpark) is beneficial but not essential. Exposure to BI platforms such as Power BI, Tableau or QuickSight, with an understanding of how backend data models support ...

platforms (AWS preferred). Exposure to data warehousing, data lakes and modern data architecture. Understanding of workflow orchestration and automation tools. Python experience (including PySpark) is beneficial but not essential. Exposure to BI platforms such as Power BI, Tableau or QuickSight, with an understanding of how backend data models ...

hands-on experience with modern cloud data platforms (e.g. Databricks or Snowflake), ideally within AWS environments. Advanced programming and data engineering skills in Python, PySpark and SQL, with a solid understanding of ETL pipelines and data transformation. Proven experience in data modelling (end-to-end), including performance optimisation using ...

working across Architecture, Engineering, Product, and Business teams. Data Engineering & Platforms Strong hands-on experience with: Databricks Delta Lake Unity Catalog dbt Spark/PySpark Data pipeline development and optimisation Lakehouse architectures ETL/ELT frameworks Enterprise data platform implementation Cloud Technologies (AWS) Experience with: Amazon S3 AWS Glue ...

Data Engineering Foundation: Practical experience with data modelling, ETL/ELT, and various database technologies. Technical Versatility: Fluency in data processing languages (Python, SQL, PySpark) and openness to tech stacks such as SSIS, Airflow, Spark, Databricks, Fabric. Cloud Capability: Experience with at least one major cloud provider (AWS, Azure ...

experience, including design and delivery of complex pipelines and data models in a cloud environment Strong proficiency in SQL; solid Python and/or PySpark capabilityDemonstrable experience with Databricks, Apache Spark, and Delta Lake architectureProven ability to design and own enterprise-grade canonical data models across multiple upstream source ...

Databricks Azure Data Lake/Lakehouse Good understanding of data architecture and data flows . Practical SQL experience. Experience with Python and/or PySpark . Experience with CI/CD and DevOps . Experience with Power BI and/or Power Apps . Strong understanding of data ingestion ...