326 to 350 of 884 ETL Jobs in London

actions, index/benchmark data, fund/account information. Awareness of typical technical landscape within modern enterprise SaaS Applications including MS Azure, Snowflake, SQL, ETL tools, and machine learning models. Knowledge of data vendors such as Bloomberg, Refinitiv, MSCI, etc., other index and ESG data providers. Hands-on experience with More ❯

visualization tools and best practices (ideally using Tableau) A n ability to manipulate data using SQL and/or Python Experience with low-code Extract Transform and Load (ETL) applications is preferrable (for example, Tableau Prep or Alteryx) Strong communication skills, with the ability to present technical concepts to non More ❯

DB objects as per the design and defined standards. Maintain/Write SQL database schemas and scripts, including views, stored procedures, dynamic queries and triggers. Extract, transform, and load (ETL) data from multiple databases to support reference data enhancements and new functionalities. Optimize database performance through indexing, query optimization, and More ❯

a structured way. Preferred Qualifications 1-3 years of using Python (or other scripting languages) for automation & data transformation. Experience managing data pipelines (e.g. extract-transform-load processes). Experience with AIML concepts. Advanced Excel proficiency (including the ability to create macros) a plus. More ❯

data migration requirements and ensure smooth data transition to the cloud platform. Develop and execute comprehensive test plans for data migrations, database implementations, and ETL processes. Perform data validation and ensure data integrity throughout the migration process. Implement integrated test automation to streamline testing procedures and increase efficiency. Utilize SQL More ❯

needs and business strategies. Cloud Architecture: Design and implement scalable, cost-effective architectures across AWS, Azure, or GCP environments. Data Engineering: Develop and manage ETL/ELT pipelines, data integration workflows, and implement CDC and delta load design for effective data management. SAP Tools: Lead SAP BW to SAP Datasphere More ❯

needs and business strategies. Cloud Architecture: Design and implement scalable, cost-effective architectures across AWS, Azure, or GCP environments. Data Engineering: Develop and manage ETL/ELT pipelines, data integration workflows, and implement CDC and delta load design for effective data management. SAP Tools: Lead SAP BW to SAP Datasphere More ❯

needs and business strategies. Cloud Architecture: Design and implement scalable, cost-effective architectures across AWS, Azure, or GCP environments. Data Engineering: Develop and manage ETL/ELT pipelines, data integration workflows, and implement CDC and delta load design for effective data management. SAP Tools: Lead SAP BW to SAP Datasphere More ❯

. Experience deploying solutions on customer infrastructure, including setting up CI/CD, databases, and containerized services. A solid understanding of data management and ETL processes; AI/ML experience is beneficial but not mandatory. Willingness to travel (up to 50%) to collaborate closely with client teams—onsite problem-solving More ❯

. Experience deploying solutions on customer infrastructure, including setting up CI/CD, databases, and containerized services. A solid understanding of data management and ETL processes; AI/ML experience is beneficial but not mandatory. Willingness to travel (up to 50%) to collaborate closely with client teams—onsite problem-solving More ❯

skills in Python and SQL Experience building NLP/ML models, particularly for text classification or sentiment analysis Background in developing data pipelines and ETL processes Proven ability to translate complex data models into business insights Experience working with large datasets (preferably e-commerce or review data) Strong communication skills More ❯

in a data environment. Understand data structures and data model (dimensional & relational) concepts like Star schema or Fact & Dimension tables, to design and develop ETL patterns/mechanisms to ingest, analyze, validate, normalize, and cleanse data. Understand and produce 'Source to Target mapping' (STTM) documents, containing data structures, business & data More ❯

science, machine learning, and business analytics Practical experience in coding languages eg. Python, R, Scala, etc.; (Python preferred) Proficiency in database technologies eg. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. pySpark, Hive, etc. Experienced working with structured and also unstructured data eg. Text, PDFs, jpgs, call recordings More ❯

science, machine learning, and business analytics Practical experience in coding languages eg. Python, R, Scala, etc.; (Python preferred) Proficiency in database technologies eg. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. pySpark, Hive, etc. Experienced working with structured and also unstructured data eg. Text, PDFs, jpgs, call recordings More ❯

science, machine learning, and business analytics Practical experience in coding languages eg. Python, R, Scala, etc.; (Python preferred) Proficiency in database technologies eg. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. pySpark, Hive, etc. Experienced working with structured and also unstructured data eg. Text, PDFs, jpgs, call recordings More ❯

translate business requirements into technical solutions. Triage and resolve complex customer issues, debugging across APIs, databases and services. Develop tooling and data pipelines for ETL projects. Recommend and implement optimisations in customer architecture and integrations for performance, scalability and maintainability. Contribute to internal documentation and reusable customer solutions libraries (FAQs More ❯

Knowledge (Snowflake, PostgreSQL, MSSQL, etc.). Scripting languages (Python, PowerShell). Data quality and resolving related processes Cloud Data Warehousing solutions and ELT/ETL solutions (e.g., Snowflake, DBT). Experience in working within an Agile environment. Experience with Automation, including Unit and Integration Testing. Knowledge of Cloud Concepts. For More ❯

Knowledge (Snowflake, PostgreSQL, MSSQL, etc.). Scripting languages (Python, PowerShell). Data quality and resolving related processes Cloud Data Warehousing solutions and ELT/ETL solutions (e.g., Snowflake, DBT). Experience in working within an Agile environment. Experience with Automation, including Unit and Integration Testing. Knowledge of Cloud Concepts. For More ❯

Strong communication skills and ability to work in cross-functional teams. Desirable: Experience working in cloud transformation projects, particularly with Azure . Knowledge of ETL/ELT processes and data governance. More ❯

and dynamic team. Responsibilities: Collaborate with various squads within the data team on project-based work. Develop and optimize data models, warehouse solutions, and ETL processes. Work with Scala, Spark, and Java to handle large-scale data processing. Contribute to manual Databricks-like data processing solutions. Requirements: Minimum of More ❯

and dynamic team. Responsibilities: Collaborate with various squads within the data team on project-based work. Develop and optimize data models, warehouse solutions, and ETL processes. Work with Scala, Spark, and Java to handle large-scale data processing. Contribute to manual Databricks-like data processing solutions. Requirements: Minimum of More ❯

and dynamic team. Responsibilities: Collaborate with various squads within the data team on project-based work. Develop and optimize data models, warehouse solutions, and ETL processes. Work with Scala, Spark, and Java to handle large-scale data processing. Contribute to manual Databricks-like data processing solutions. Requirements: Minimum of More ❯

stakeholders to provide timely and actionable insights. Main Responsibilities Engineering : Developing and maintaining business-critical data ingestion, analysis, and management systems. Utilizing API and ETL methods to integrate with internal systems. Building tools to minimize errors and improve customer experience. Automating data development and governance to improve data health. Designing More ❯

stakeholders to provide timely and actionable insights. Main Responsibilities Engineering : Developing and maintaining business-critical data ingestion, analysis, and management systems. Utilizing API and ETL methods to integrate with internal systems. Building tools to minimize errors and improve customer experience. Automating data development and governance to improve data health. Designing More ❯

Exposure to multi-platform integration (MS tools preferred). On premise SQL environments, legacy SSIS, SSRS and other SQL-related technologies employed in complex ETL or ELT patterns Synapse Link for Dataverse Dataverse, Data Flows, Cloud Flows, DAX and Power Platform implementations Synchronisation methods for Synapse and Fabric from D365 More ❯