1 to 25 of 419 DemandTrendPermanent ETL Jobs in London

closely collaborate with data scientists, analysts, and software engineers to ensure efficient data processing, storage, and retrieval for business insights and decision-making. From their expertise in data modelling, ETL (Extract, Transform, Load) processes, and big data technologies it becomes possible to develop robust and reliable data solutions. RESPONSIBILITIES Data Pipeline Development: Design, implement, and maintain scalable data pipelines for … sources using tools such as databricks, python and pyspark. Data Modeling: Design and optimize data models and schemas for efficient storage, retrieval, and analysis of structured and unstructured data. ETL Processes: Develop and automate ETL workflows to extract data from diverse sources, transform it into usable formats, and load it into data warehouses, data lakes or lakehouses. Big Data Technologies … teams, including data scientists, analysts, and software engineers, to understand requirements, define data architectures, and deliver data-driven solutions. Documentation: Create and maintain technical documentation, including data architecture diagrams, ETL workflows, and system documentation, to facilitate understanding and maintainability of data solutions. Best Practices: Continuously learn and apply best practices in data engineering and cloud computing. QUALIFICATIONS Proven experience as More ❯

our data solutions are secure, efficient, and optimized. Key Responsibilities: Design and implement data solutions using Azure services, including Azure Databricks, ADF, and Data Lake Storage. Develop and maintain ETL/ELT pipelines to process structured and unstructured data from multiple sources. - Automate loads using Databricks workflows and Jobs Develop, test and build CI/CD pipelines using Azure DevOps More ❯

experience in... Extensive experience in implementing solutions around Databricks, Azure Data Factory, SQL, Python Excellent understanding of Microsoft SQL Server and data modelling,An understanding of SQL, T-SQL, ETL/ELT, and Data Pipelines.Previous exposure in a Data Engineering capacity is highly sought after. Strong hands-on experience in Data Warehouse and Data Lake technologies preferably around Azure. This More ❯

Technical Business Analysis experience. A proactive awareness of industry standards, regulations, and developments. Ideally, you'll also have: Experience of Relational Databases and Data Warehousing concepts. Experience of Enterprise ETL tools such as Informatica, Talend, Datastage or Alteryx. Project experience using the any of the following technologies: Hadoop, Spark, Scala, Oracle, Pega, Salesforce. Cross and multi-platform experience. Team building More ❯

Advanced SQL skills and experience architecting solutions on modern data warehouses (e.g., Snowflake, BigQuery, Redshift). Hands-on experience with advanced modelling techniques in dbt. A deep understanding of ETL/ELT processes and tools (e.g., Fivetran, Airbyte, Stitch). Experience with data visualisation tools (e.g., Mode, Looker, Tableau, Power BI) and designing robust BI semantic layers. Exceptional understanding of More ❯

is an exciting opportunity for an ambitious individual to contribute to our success while gaining valuable experience in a fast-paced and innovative environment. Responsibilities: - Implement data modelling and ETL processes to integrate data from different sources into fabric data lake/data warehouse. - Collaborate with the data and analytics team to design, develop, and maintain Power BI reports and More ❯

is an exciting opportunity for an ambitious individual to contribute to our success while gaining valuable experience in a fast-paced and innovative environment. Responsibilities: - Implement data modelling and ETL processes to integrate data from different sources into fabric data lake/data warehouse. - Collaborate with the data and analytics team to design, develop, and maintain Power BI reports and More ❯

markets, balancing mechanisms, and regulatory frameworks (e.g., REMIT, EMIR). Expert in Python and SQL; strong experience with data engineering libraries (e.g., Pandas, PySpark, Dask). Deep knowledge of ETL/ELT frameworks and orchestration tools (e.g., Airflow, Azure Data Factory, Dagster). Proficient in cloud platforms (preferably Azure) and services such as Data Lake, Synapse, Event Hubs, and Functions. More ❯

and product to ensure data and reporting needs are met Implement data quality checks, data governance practices, and monitoring systems to ensure reliable and trustworthy data. Optimize performance of ETL/ELT workflows and improve infrastructure scalability. More ❯

and product to ensure data and reporting needs are met Implement data quality checks, data governance practices, and monitoring systems to ensure reliable and trustworthy data. Optimize performance of ETL/ELT workflows and improve infrastructure scalability. More ❯

and product to ensure data and reporting needs are met Implement data quality checks, data governance practices, and monitoring systems to ensure reliable and trustworthy data. Optimize performance of ETL/ELT workflows and improve infrastructure scalability. More ❯

and product to ensure data and reporting needs are met Implement data quality checks, data governance practices, and monitoring systems to ensure reliable and trustworthy data. Optimize performance of ETL/ELT workflows and improve infrastructure scalability. More ❯

drift, and anomalies using Azure Monitor or other observability tools. * Schedule and automate model retraining pipelines to maintain performance over time. 3. Data Engineering & Preprocessing * Develop and maintain scalable ETL/ELT data pipelines using Azure Data Factory, Data Lake, and SQL. * Prepare and preprocess large datasets to ensure data quality and readiness for ML modelling and analytics workflows. 4. More ❯

drift, and anomalies using Azure Monitor or other observability tools.* Schedule and automate model retraining pipelines to maintain performance over time. 3. Data Engineering & Preprocessing * Develop and maintain scalable ETL/ELT data pipelines using Azure Data Factory, Data Lake, and SQL.* Prepare and preprocess large datasets to ensure data quality and readiness for ML modelling and analytics workflows. 4. More ❯

and product to ensure data and reporting needs are met Implement data quality checks, data governance practices, and monitoring systems to ensure reliable and trustworthy data Optimize performance of ETL/ELT workflows and improve infrastructure scalability Qualifications Experience Relevant experience in data engineering and solution delivery, with a strong track record of technical leadership Deep understanding of data modeling More ❯

Spark, Databricks, or similar data processing tools. Stron g technical proficiency in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based More ❯

data models and user behavioral data accessible and reusable for data professionals throughout the company. In this role, your main responsibilities and deliverables will be to build and maintain ETL pipelines, create foundational data models in our data warehouse, and integrate new data sources using DataOps best practices, including documentation and testing. You will also support other data specialists in More ❯

or Scala. - Expertise in SQL and experience with both relational and NoSQL databases. - Familiarity with cloud platforms (e.g., AWS) and their services. - Knowledge of data modeling, data warehousing, and ETL design patterns. - Experience with version control systems (e.g., Git) and CI/CD pipelines . - Strong problem-solving skills and attention to detail. - Excellent communication skills and ability to work More ❯

architectures that drive measurable transformation aligned to banking sector priorities. Client Advisory & Engagement – Act as a trusted advisor, leading workshops and influencing senior stakeholders on strategy, roadmaps, and innovation. ETL/ELT & Data Engineering – Provide oversight on robust data pipeline and integration designs using SSIS, ADF, Informatica, or IBM DataStage. Data Governance & Quality – Define governance, metadata, and quality frameworks using More ❯

architectures that drive measurable transformation aligned to banking sector priorities. Client Advisory & Engagement – Act as a trusted advisor, leading workshops and influencing senior stakeholders on strategy, roadmaps, and innovation. ETL/ELT & Data Engineering – Provide oversight on robust data pipeline and integration designs using SSIS, ADF, Informatica, or IBM DataStage. Data Governance & Quality – Define governance, metadata, and quality frameworks using More ❯

architectures that drive measurable transformation aligned to banking sector priorities. Client Advisory & Engagement – Act as a trusted advisor, leading workshops and influencing senior stakeholders on strategy, roadmaps, and innovation. ETL/ELT & Data Engineering – Provide oversight on robust data pipeline and integration designs using SSIS, ADF, Informatica, or IBM DataStage. Data Governance & Quality – Define governance, metadata, and quality frameworks using More ❯

architectures that drive measurable transformation aligned to banking sector priorities. Client Advisory & Engagement – Act as a trusted advisor, leading workshops and influencing senior stakeholders on strategy, roadmaps, and innovation. ETL/ELT & Data Engineering – Provide oversight on robust data pipeline and integration designs using SSIS, ADF, Informatica, or IBM DataStage. Data Governance & Quality – Define governance, metadata, and quality frameworks using More ❯

plus. Experience in data modeling and warehouse architecture design. Experience with data orchestration and workflow management tools such as Airflow. Solid understanding of data ingestion, transformation, and ELT/ETL practices. Expertise on data transformation frameworks such as DBT. Experience with Infrastructure as Code (IaC) toolsTerraform preferred. Experience with version control systems, GitHub preferred. Experience with continuous integration and delivery More ❯

Engineer, your role involves designing, implementing, and managing data solutions on the Microsoft Azure cloud platform. Your responsibilities will include, but not limited to: Data Integration Data Integration and ETL (Extract, Transform, Load): You'll develop and implement data integration processes to extract data from diverse sources and load it into data warehouses or data lakes. Azure Data Factory is … a key tool for building efficient ETL pipelines. Your goal is to ensure that data is effectively cleaned, transformed, and loaded. Your sources will include Rest API from various systems. Being able to onboard data from API's is essential. Use of Postman for testing outside of ADF is a preferred skill. Data Engineering As an Azure Data Engineer, you More ❯

in energy trading environments, particularly Natural Gas and Power markets. Expert in Python and SQL; strong experience with data engineering libraries (e.g., Pandas, PySpark, Dask). Deep knowledge of ETL/ELT frameworks and orchestration tools (e.g., Airflow, Azure Data Factory, Dagster). Proficient in cloud platforms (preferably Azure) and services such as Data Lake, Synapse, Event Hubs, and Functions. More ❯