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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Ascent Sourcing - **Location:** London, UK - **Salary:** £50,000.0 - £60,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Confluence, JIRA, Business Software, Cloud Computing, Customer Data Management, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Data Mapping, Data Migration, Data Structures, Data Systems, Python (Programming Language), Salesforce.Com, SQL Databases, Tableau (Software), Cloud Platform System, Apache Spark, Data Analytics, Data Management, Tools for Reporting, Hubspot, Interactive Whiteboards, Data Pipelines, Legacy Systems, Mulesoft, Databricks - **Published:** June 29, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=04c808a1b3729d94 ## About the Role We are seeking an experienced Data Engineer with strong Databricks experience to join a growing Data & Analytics team. As part of this continued growth, the successful candidate will play a key role in developing and enhancing data solutions, working closely with the Data & Analytics Manager and wider team to support the delivery of data-driven initiatives. This is an excellent opportunity for a skilled Data Engineer looking to make an impact within a forward-thinking organisation. The Data & Analytics team is in the early stages of implementing Databricks, with the ambition of creating a centralised data platform that will support multiple business applications, including Salesforce, HubSpot, and other key data sources. This is a standalone role offering the opportunity for an experienced Data Engineer to take ownership of the Databricks environment, establish best practices, and help shape the future data platform. We are looking for someone who is motivated, proactive, and excited by the opportunity to build something from the ground up. Initially, the focus of the role will be centred around data governance, platform setup, and establishing robust standards and processes, with technical support provided by a third-party partner. As the roadmap progresses, the role will move towards designing and developing data pipelines and expanding the Databricks platform., Proven commercial experience as a Data Engineer within a modern data platform environment. Strong hands-on experience with Databricks or similar cloud-based data platforms. Demonstrable experience designing and building production-grade data pipelines. Experience working with data migration and mapping from legacy systems. Strong understanding of data modelling, transformation, and data engineering principles. Experience supporting analytics and reporting platforms such as Tableau. Desirable: Experience working with Salesforce Data Cloud (Data 360) or CRM data models. Desirable: Exposure to AI-ready data platforms, automation, or emerging data use cases. Desirable: Experience working within a Centre of Excellence or collaborative multi-team environmen ## Description Design, build, and maintain scalable, production-ready data pipelines within Databricks. Develop ingestion, transformation, and optimisation solutions using SQL, Spark, and Python. Ensure data platforms and datasets are reliable, performant, and ready to support analytics and business intelligence. Lead hands-on data mapping activities from legacy systems into Databricks, considering downstream analytics requirements including Tableau and future AI-driven use cases such as Mulesoft IDP and Data360/Agentforce. Support the ingestion, analysis, and integration of data from mergers and acquisitions into the central data platform. Assess acquired datasets and ensure successful integration into existing data structures. Establish and maintain testing standards across functional, regression, integration, and user acceptance testing. Implement data quality frameworks, validation processes, and monitoring within data pipelines. Support the development and implementation of governance standards defined by the Data & Analytics Manager. Maintain high-quality documentation and ensure strong data governance practices. 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