18 of 18 Permanent ETL Jobs in West End of London

cloud data warehouse, data lake or data platform) to enable digital transformation \n\n \n Working with clients on data warehousing, building operational ETL/ELT data pipelines across a number of sources, and constructing relational and dimensional data models \n\n \n Performing maturity assessments across clients' data capabilities ...

and standardisation: Experience in resolving data quality issues and ensuring data accuracy through cleansing and standardisation techniques.Use of data integration tools: Skilled in using ETL tools for data integration and storage. Ensures data interoperability with other datasets.Collaboration with data professionals: Experience collaborating with other data professionals to improve modelling and ...

with a drive for continuous improvementFamiliarity with prompt engineering principles and LLM-based toolsEffective communication skills to present data findings and support usersExperience with ETL/ELT processes and architecture in data pipelinesExperience building models for dashboard or cube consumptionFamiliarity with cloud-based data lake platforms such as AWS, Azure ...

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 and dbt.Ability to provide clear input, guide, empower junior members of the team to achieve desired outcomes.Familiarity ...

enable self-service of data analytics to data hungry internal users as well as automation of all our data workflows - from simple ETL jobs to ML training and prediction. \n The data team at OEV is growing at speed. We have invested time and resources on establishing a core data ...

nAgile Delivery: Experienced in Scrum/Kanban environments, participating in ceremonies (Dailies, Refinement, Retrospectives) and managing tasks via backlogs.Data Engineering Best Practices: Knowledge of ETL/ELT patterns, data versioning, and building robust, automated pipelines. You'll ensure data security and compliance requirements are met with solutions you propose and ...

actionable and scalable. \n • Expertise of using Spark, Dremio, Teradata or other SQL technologies. \n • Strong knowledge and experience of using business intelligence, ETL frameworks and visualisation tools such as Tableau. \n • Experience with Python, GitHub, data orchestration tools (Oozie, Airflow) for data wrangling, building data pipelines and building analytical ...

scale data processing solutions and deploying into a production environment \n • Proficient in SQL and SQL extensions for analytical queries \n • Solid understanding of ETL/ELT data processing pipelines and design patterns \n • Aware of key features and pitfalls of distributed data processing frameworks, data stores and data serialisation ...

and modern data platform technologies. Expertise in data architecture, data warehousing, and large-scale data platforms. Strong knowledge of Data Mesh , database design, and ETL/ELT methodologies. Experience designing scalable, secure, and governed data solutions. Understanding of data security, compliance, and GDPR requirements. Excellent analytical and problem-solving abilities. ...

and managing the delivery of complex multi-team initiatives within a technical landscape bridging the gap between business strategy & engineering execution including:\n \n ETL/ELT & Data Pipeline knowledge \n \n Data Modelling & Architecture \n Data Quality & GovernanceData observabilityCloud data ecosystemsPrior experience of managing a team of delivery professionals ...

into practical solutionsYou're proactive about improving documentation, governance, workflows, and system reliability\n Nice to have: \n \n Braze technical implementation experience \nReverse ETL/activation tooling (e.g. Hightouch)API or integration engineering experienceHTML/CSS/Liquid or templating familiarity\n Additional Information \n For everyone, everywhere. ...

engineering, analytics engineering, or related disciplines delivering enterprise data solutions \nStrong proficiency in Databricks, SQL, and Python/PySpark, including building, optimizing, and troubleshooting ETL pipelinesExperience designing scalable, maintainable data architectures and dimensional models for BI, reporting, and analytical use casesProven ability to partner with business stakeholders to gather requirements … documented datasets aligned to agreed definitions and business expectationsAct as a trusted technical partner, balancing delivery speed, data quality, and long-term scalabilityArchitect modular ETL pipelines to improve maintainability, reuse, and traceabilityDefine and uphold engineering standards across coding, testing, documentation, and version control practicesSupport BI lakehouse development and integration across ...

analytical skillset on a variety of internal and external data. Leads, instructs, and mentors newer Data Engineering crew. \n Core Responsibilities \n 1. Writes ETL (Extract/Transform/Load) processes, designs database systems and, develops tools for real-time and offline analytic processing. \n 2. Troubleshoots software and processes ...

modern Python expertise; you write clean, idiomatic, well-tested code and hold others to the same standard Some experience building and maintaining data pipelines, ETL processes, or data-intensive backend services Solid understanding of FastAPI or a comparable modern Python web framework Strong automated testing skills across unit, integration, and ...

build data models, schemas to support business requirementsDevelop and maintain data ingestion and processing systems using various tools and technologies, such as SQL, NoSQL, ETL, Luigi, Airflow, Argo, etc.Implement data storage solutions using different types of databases, such as relational, non-relational, or cloud-based.Working collaboratively with the client and … and relational databases (e.g. MS SQL/Azure SQL, PostgreSQL) You have framework experience within either 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 ...

Defining and enforcing development best practice and coaching team members to ensure consistency. Working with project architects, taking responsibility for non-functional needs of ETL/ELT data processing pipelines such as robustness and performance. Taking responsibility for standards and execution of unit and integration testing done within the team. … experience in one of Java, Scala, or Python Software development experience with data-processing platforms from vendors including Informatica, Azure Databricks or any relevant ETL tools Expert in SQL or SQL extensions for analytical use case Expert understanding of distributed data stores and data processing frameworks Ability to simply and ...

commercial performance metrics, and content derived from tens of thousands of expert interviews. The role gives you the mandate to find new ways to extract value from that data, with the freedom to experiment and the expectation to ship. \n Key Responsibilities: \n \n Design and build PoC solutions with … clear 'ship-or-kill' decision framework, defining evaluation criteria upfront so success is measurable, not just qualitative. \nBuild extraction, transformation, and loading (ETL/ELT) pipelines to create net-new datasets for analytical or ML use, including feature engineering pipelines for model training and downstream reporting.Apply supervised and unsupervised ...

commercial performance metrics, and content derived from tens of thousands of expert interviews. The role gives you the mandate to find new ways to extract value from that data, with the freedom to experiment and the expectation to ship. \n Key Responsibilities: \n \n Design and build PoC solutions with … clear 'ship-or-kill' decision framework, defining evaluation criteria upfront so success is measurable, not just qualitative. \nBuild extraction, transformation, and loading (ETL/ELT) pipelines to create net-new datasets for analytical or ML use, including feature engineering pipelines for model training and downstream reporting.Apply supervised and unsupervised ...