1 to 25 of 47 Permanent PySpark Jobs in the UK excluding London

office. About you : You have strong Python backend software engineer skills You have experience working with large data sets You have experience of using PySpark and ideally also Apache Spark You believe in automating wherever possible You're a collaborative problem solver with great communication skills Other technology

Days Cycle to work scheme And More.. DATA ENGINEER - ESSTENTIAL SKILLS Proven experience building data pipelines using Databricks . Strong understanding of Apache Spark (PySpark or Scala) and Structured Streaming . Experience working with Kafka (MSK) and handling real-time data . Good knowledge of Delta Lake/Delta

demand delivery Provide technical guidance for team members Providing 2nd or 3rd level technical support About You Experience using SQL, SQL Server DB, Python & PySpark Experience using Azure Data Factory Experience using Data Bricks and Cloudsmith Data Warehousing Experience Project Management Experience The ability to interact with the operational

integrations using AWS PaaS such as Glue, EMR, Sagemaker, Redshift, Aurora and Snowflake Building data processing and analytics pipelines as code, using Python, SQL, PySpark, spark, CloudFormation, lambda, step functions, Apache Airflow Monitoring and reporting on the data platform performance, usage and security Designing and applying security and access

science, analytics, and ML solutions. Continuously optimise data workflows for performance, reliability, and scalability. What youll need: Proven hands-on experience with Databricks, Python, PySpark, and SQL. Machine learning experience in a cloud environment (AWS, Azure, or GCP). Strong understanding of ML libraries such as scikit-learn, TensorFlow

analytics, and ML solutions. • Continuously optimise data workflows for performance, reliability, and scalability. What you’ll need: • Proven hands-on experience with Databricks, Python, PySpark, and SQL. • Machine learning experience in a cloud environment (AWS, Azure, or GCP). • Strong understanding of ML libraries such as scikit-learn, TensorFlow

Fabric. Migrate on-prem data to Fabric Lakehouse/Warehouse, enabling Power BI analytics. Develop robust data pipelines, notebooks, and data models using: Python, PySpark, SQL, Synapse, and Data Warehousing principles. Implement CI/CD processes with GitHub integration for lifecycle management. Ensure governance, security, and best practices across

Fabric. Migrate on-prem data to Fabric Lakehouse/Warehouse, enabling Power BI analytics. Develop robust data pipelines, notebooks, and data models using: Python, PySpark, SQL, Synapse, and Data Warehousing principles. Implement CI/CD processes with GitHub integration for lifecycle management. Ensure governance, security, and best practices across

Continuously improve data systems and maintain awareness of best practice and emerging approaches Essential Skills Experience with large-scale analytics engines (e.g. Spark/PySpark) and scripting languages such as Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage

data models and analytics; exposure to predictive modelling and machine learning is a plus. Proficient in SQL and Python, with bonus points for PySpark, SparkSQL, and Git. Skilled in data visualisation with tools such as Tableau or Power BI. Confident writing efficient code and troubleshooting sophisticated queries. Clear

Methodology or other similar dimensional modelling standards, is essential to the role. Technical experience building and deploying models and reports utilizing the following tools: PySpark Microsoft Fabric or Databricks Power BI Git CI/CD pipelines (Azure DevOps experience preferred) An understanding of the structure and purpose

manipulate data and building additional process to support data processing activities Experience manipulating, processing and storing data using tools such as SQL, dbt, pyspark and SSIS Comfortable building out and administrating data models, ensuring that they are accessible and used appropriately, leveraging platforms such as Databricks and Power

/Data Engineering role Large and complex datasets Azure, Azure Databricks Microsoft SQL Server Lakehouse, Delta Lake Data Warehousing ETL Database Design Python/PySpark Azure Blob Storage Azure Data Factory Desirable: Exposure ML/Machine Learning/AI/Artificial Intelligence

build skills and knowledge relevant to the role. Essential Skills Experience of using a unified engine for large-scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical

Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning environments Strong programming skills in Python, with hands-on experience in PySpark and SQL Deep understanding of ML lifecycle management and CI/CD best practices Familiarity with cloud-native ML platforms and scalable deployment strategies

using Databricks, Azure, and Power BI to turn complex datasets into reliable, insight-ready assets. You'll apply strong skills in SQL, Python, and PySpark to build efficient ELT workflows and ensure data quality, performance, and governance. Collaboration will be key as you partner with analysts and business teams

data pipelines using Azure services such as Synapse, Data Factory or Databricks Commercial experience with Microsoft Fabric Strong understanding of SQL and Python/PySpark Experience with Power BI and data modelling Some of the package/role details include: Salary up to £85,000 Flexible hybrid working model

product teams who'll challenge them in the best ways The ideal candidate will: Know their way around AWS data tools (Glue/PySpark, Athena) and Microsoft Fabric Be able to write clean Python and SQL in their sleep Have battle scars from integrating CRMs (HubSpot, Salesforce) via APIs

DevOps and broader software engineering standards. Cloud experience (any platform). Previous mentoring experience. Nice to have: Snowflake or Databricks Spark, PySpark, Hadoop or similar big data tooling BI exposure (PowerBI, Tableau, etc.) Interview Process Video call - high-level overview and initial discussion In-person technical presentation - based

team by creating and maintaining data solutions for KPI reporting. Developing scalable, performance-optimised ELT/ETL pipelines using T-SQL, Python, ADO, C#, PySpark and Jupyter Notebooks. Working with the Gazetteer and GIS teams to maintain stable, consolidated database platforms for mapping and GIS systems. Contributing

reviews via Tabular Editor. Excellent design intuition-clean layouts, drill paths, and KPI logic. Nice to Have Python for automation or ad-hoc prep; PySpark familiarity. Understanding of Lakehouse patterns, Delta Lake, metadata-driven pipelines. Unity Catalog/Purview experience for lineage and governance. RLS/OLS implementation experience.

reviews via Tabular Editor. Excellent design intuition-clean layouts, drill paths, and KPI logic. Nice to Have Python for automation or ad-hoc prep; PySpark familiarity. Understanding of Lakehouse patterns, Delta Lake, metadata-driven pipelines. Unity Catalog/Purview experience for lineage and governance. RLS/OLS implementation experience.

data analytics or credit risk role, ideally within utilities, financial services or other regulated industry Strong coding skills in SQL, Python and PySpark for data extraction, transformation, modeling and forecasting Solid understanding of forecasting techniques, scenario modelling, and regression-based analytics Strong commercial acumen, with the ability to translate

Strong knowledge of AWS data services (Glue, S3, Lambda, Redshift, etc.) Solid understanding of ETL processes and data pipeline management Proficiency in Python and PySpark Experience working with SQL-based platforms Previous involvement in migrating on-premise solutions to cloud is highly desirable Excellent collaboration skills and ability

mentoring experience, with a genuine commitment to team growth and wellbeing. Strong hands-on skills in: AWS (or equivalent cloud platforms) Python/PySpark for data engineering and automation TypeScript, Node.js, React.js for full-stack development Solid grasp of distributed systems design, secure coding, and data privacy principles. Familiarity