Data Engineer

• Providing SME guidance in traditional data architecture disciplines around data structures, data flows, data sourcing and data governance
• Work closely with clients to understand their data requirements and take responsibility for ensuring solutions are fit for purpose
• Provide technical leadership for the rest of the team in the area of data
• Work at the solution or enterprise level - for example, resolving data definition and mastering issues across complex stakeholder environments
• Manage, coach and develop a small number of staff, with a focus on managing employee performance and assisting in their career development
• Provide direction and leadership for your team as you solve challenging problems together

Data Architect

• Providing the organization’s data consumers high quality data sets by data curation, consolidation, and manipulation from a wide variety of large scale (terabyte and growing) sources.
• Building high quality data pipelines and ETL processes that interact with terabytes of data on leading platforms such as Snowflake and BigQuery.
• Developing and improving our enterprise data marts by creating efficient and scalable data models to be used across the organization.
• Partnering with our analytics, data science, crm, and machine learning teams for data solutions.
• Responsible for an enterprise data mart integrity, validation, and documentation.
• Responsible for the data pipelines’ SLA and dependency management.
• Writing technical documentation for data solutions, and presenting at design reviews
• Solving data pipeline failure events and implementing sound anomaly detection
• Working with various teams from analytics to product owners and front end developers on tracking solutions and solving technical challenges
• Leading medium to large size projects in terms of writing technical specs and project planning
• Mentoring junior members of the team.

Senior Data Engineer

• Own and optimise our platform architecture (improve reliability, scalability, maintainability, etc)
• Shape the direction of our growing team and coach team members on best practices.
• Shape engineering outputs and raising the technical bar of the organization without losing touch with hands-on engineering
• Interact with Senior Leadership to present your data projects back to them
• Build new data sources and data pipelines that deliver key data and insights to the business (this will include integration with third party systems and ingestion through APIs)
• Scope and deliver new data engineering projects in collaboration with business stakeholders
• Develop and deploy ML infrastructure to help build out our ever-growing AI requirements and use cases
• Collaborate closely with our dev teams to build seamless integrations between our back-end databases and our data platform

Data Engineer – Tech Lead

• Participate in designing, developing and maintaining activities for LCCC DataWarehouse ensuring reliable data across the organisation
• Source, model and provide reliable data in a form that is ready for report/dashboard building
• Follow the data governance standards
• Build and maintain key data governance and management artefacts e.g., Flow Diagram, Data Dictionary.
• Identify patterns, anomalies, and structure of data in preparation for Extract Load and Transformation.
• Perform data validation, quality checks, and reconciliation across source and target systems.
• Define the low-level design of a data warehouse to ensure robustness e.g., restorability, traceability, ease to support, etc.
• Help design and maintain the modern DataWarehouse architecture, implementation, administration, and support

• Translate legacy ETL logic from SSIS and stored procedures into modern **ELT pipelines using dbt**
• Implement **Data Vault 2.0 structures** including Raw Vault and Business Vault
• Build **datamarts and curated datasets** for downstream analytics and reporting
• Design and operate workflows using **Dagster**, including scheduling, dependencies, and recovery mechanisms
• Deploy and run data workloads on **OpenShift / Kubernetes environments**
• Enable **near real-time data processing** using Kafka-triggered pipelines
• Integrate with upstream data lake environments and external data providers
• Establish robust **data validation and reconciliation processes**
• Implement automated testing and monitoring using dbt
• Support production pipelines and resolve incidents when required
• Create clear documentation and ensure operational readiness
• Continuously improve performance, reliability, and maintainability

• Assist in translating legacy ETL processes into modern ELT pipelines using dbt
• Support the development of Data Vault 2.0 models
• Build and maintain data transformations and curated datasets
• Contribute to data integration and migration activities
• Support workflow development using Dagster
• Assist in deploying and monitoring workloads on OpenShift / Kubernetes
• Participate in pipeline monitoring and operational support
• Help implement automated data validation and testing
• Support reconciliation between legacy and modern platforms
• Investigate and resolve data quality issues together with senior team members
• Document technical solutions and operational procedures
• Learn and apply software engineering best practices
• Contribute ideas for improving performance and maintainability
• Participate in code reviews and team knowledge sharing

Junior Data Engineer

• Design, build, and own Preply’s data lake.
• Develop and operate scalable, reliable batch and streaming ingestion pipelines.
• Define and implement data contracts between producers and consumers.
• Build enrichment logic that joins, standardizes, and contextualizes data across domains.
• Instrument ingestion pipelines with strong observability: freshness, latency, data quality, and cost metrics.
• Apply consistent access control, classification, and privacy protections at ingestion time.
• Contribute to standardized ingestion templates, shared libraries, and platform tooling that enable teams to onboard new data sources independently.
• Work closely with Product, Backend, Analytics, and ML partners to align on ingestion requirements.

Senior Data Engineer - Data Ingestion, Enrichment

• Write, ship, and maintain production code as a hands-on engineer.
• Architect, design, and implement large-scale distributed data systems and pipelines.
• Contribute to technical decision-making across batch and streaming data solutions.
• Collaborate with engineers, product managers, data scientists, and intelligence analysts to build customer-focused products.
• Explore and integrate new technologies (e.g. data orchestration or cloud-native tools) to optimise performance and scalability.
• Take shared ownership of data systems, from design to deployment and ongoing improvement and support.
• Perform thoughtful peer reviews that raise code quality and share best practices across the team.
• Contribute to platform-wide initiatives that improve reliability, observability, and cost efficiency.
• Help shape the technical roadmap for data engineering across Elliptic.
• Leverage and deploy AI and agentic systems to operate more effectively.

Senior Software Engineer – Data

• Build and maintain reliable, scalable data pipelines to support digital enablement, reporting, analytics, and data science
• Work closely with analysts, data scientists, and business teams to understand data requirements
• Support and optimise our Databricks platform, ensuring it is used efficiently and at scale
• Develop and maintain ETL/ELT processes to integrate data from multiple systems
• Identify and fix performance issues in data pipelines and queries
• Ensure data quality, security, and governance standards are met
• Create clear documentation for data pipelines and platform design

• Own the strategy, roadmap, and data models for the core services like applications, identity, and access services, and the API gateway that exposes them
• Working with the API Governance lead, expose these services through clean, well-versioned APIs, and evolve them without breaking the teams and customers that depend on them
• Own and monetise the data products built on the platform, such as analytics and reporting APIs, from discovery through launch and iteration
• Own the enterprise capabilities that take Yapily upmarket: single sign-on, role-based access, audit, usage and entitlements, and multi-organisation support
• Extend the identity and access model to AI agents and machine identities, with scoped, time-bound, revocable, and auditable credentials
• Provide the authorisation foundations for agent access, including OAuth flows for MCP and federated access that stays within the consent a customer or end user has granted
• Own the platform's integrations with the systems the business runs on, such as billing, CRM, and compliance tooling, keeping accounts, usage, and entitlements consistent across them (Lead to Cash)
• Balance the competing demands of multiple product teams and customers, and prioritise foundational investment against near-term requests
• Partner with engineering to scope and ship, make the build-versus-buy calls, and work with security, privacy, and legal so the platform meets its regulatory obligations
• Partner with commercial and marketing on pricing, packaging, and go-to-market, giving them the product view of what the platform can do and what is coming
• Define and track the platform metrics that matter: adoption across product teams, reliability and latency, data quality, and the satisfaction of the teams and customers building on the platform

Product Manager – Core Platform, Data

• Design, build, and maintain ETL/ELT pipelines
• Ingest data from internal systems and external APIs
• Develop scalable data processing workflows (batch and/or real-time)
• Ensure pipelines are reusable, efficient, and maintainable
• Integrate data from various sources, including transactional flows
• Handle complex data scenarios such as: Event sequencing (e.g. bet → resolve), Idempotency and duplicate handling, Partial or delayed data
• Ensure consistency between source systems and analytical datasets
• Implement transformation logic aligned with architectural data models
• Build and maintain structured data layers for analytics consumption
• Collaborate closely with the Data Architect on model implementation
• Implement data validation, monitoring, and alerting mechanisms
• Identify and resolve data inconsistencies or failures
• Ensure high levels of data accuracy and availability
• Optimise pipelines for performance and cost-efficiency
• Support scaling of data infrastructure as volumes grow
• Ensure low-latency data availability where required
• Work closely with Data Architect, Analysts, and Manager
• Support Analysts by ensuring availability of curated datasets
• Contribute to continuous improvement of data platform capabilities

• Assist in designing, planning and managing the data migration process.
• Manage assigned risks and monitor potential impacts as part of the data migration plan.
• Work with subject matter experts and project team to identify, define, collate, document and communicate the data migration requirements.
• Work independently as a self-starter, and within a team environment.
• Work across multiple functional projects to understand data usage and implications for data migration.
• Develop best practice, processes, and standards for effectively carrying out data migration activities.
• Work with customers in gathering technical requirements for data migration needs.
• Share knowledge with team members and develop customer solutions efficiently.
• Perform source system data analysis in order to manage source to target data mapping.
• Perform migration and testing of static data and transaction data from one core system to another.

Data Migration Consultant

• Managing Data workstream on large S/4HANA programmes
• Working with SAP functional and technical consultants analysing data migration requirements
• Leading teams to design, build, test, and execute data migrations
• Assisting in the financial management and control of client engagements
• Working in a team across multiple areas of Data and Analytics such as data and reporting strategy, reporting and KPIs definition, data governance, data quality
• Being actively involved in presales/business development activities to identify and research opportunities on new/existing clients
• Developing project strategies to solve complex technical challenges for our clients
• Contributing to managing and delivering large projects by developing the project team, assessing engagement risks throughout, driving conclusions, and reviewing/challenging the output produced by the team

SAP S/4HANA Data Migration Manager

• Define and maintain the enterprise data architecture, including target-state designs, roadmaps, and architectural standards and patterns.
• Own the architecture of our Databricks Lakehouse platform, including workspace design, Unity Catalog structure, medallion architecture, and environment strategy.
• Design and govern integration patterns for sharing data with external clients and third parties, including secure, scalable approaches using Databricks Delta Sharing, APIs, and file-based exchange where appropriate.
• Lead the design and adoption of Master Data Management (MDM), including entity definitions, golden-record strategy, match-and-merge rules, and stewardship processes.
• Establish standards and practices for optimising Databricks performance and cost — cluster and compute strategy, Delta table, caching, and workload right-sizing.
• Define data modelling standards (conceptual, logical, physical) across analytical and operational domains.
• Work with data governance colleagues to embed data quality, lineage, cataloguing, classification, and access-control policies into platform design.
• Ensure architectures meet security, privacy, and regulatory requirements, including GDPR and data residency considerations.
• Review solution designs, provide architectural assurance, and support engineering teams through design authority and peer review.
• Design data architectures that enable AI adoption — ensuring data is discoverable, well-governed, and AI-ready for machine learning, GenAI, and agentic use cases across the business.
• Partner with AI/ML teams to define patterns for feature engineering, model data pipelines, vector search, and the safe exposure of enterprise data to AI systems.
• Evaluate emerging technologies and make pragmatic build/buy/adopt recommendations, including data and AI enablement opportunities.

Enterprise Data Architect

• To architect robust data ingestion streams for real time data processing, marketing technology and analytics
• Contribute to the platform logical and physical data models
• Develop ETL, ELT routines for structured analytics
• Develop a range of integrations to APIs, flat files and other external systems to support broader ecosphere
• Work with software developers to enhance gaming platform integration
• Support testing and deployment
• Develop automated monitoring, fault recovery and diagnosis
• Document output to a high level
• Mentor and train localised application support specialists
• Lead the development of AWS data solutions
• Lead adoption of new integration and data technologies
• Input into the AWS technology roadmap and strategy
• Champion best practice and standardisation of development
• Deliver best in class solutions in terms of robustness, recovery and resilience
• Deliver continuous improvement to development methodologies and automation

• Designing and building scalable data pipelines that ingest healthcare data from multiple providers into trusted research environments and data platforms.
• Developing and maintaining data transformation logic in Python to create high-quality, curated and research-ready datasets.
• Contributing to complex, shared codebases while following engineering best practices including code reviews, testing and clean coding standards.
• Collaborating with data scientists, epidemiologists, software engineers and product teams to understand requirements and deliver impactful data solutions.
• Processing large-scale healthcare datasets and implementing robust data quality, validation and governance controls.
• Transforming and harmonising healthcare data into common data models such as OMOP and FHIR to support research and analytical use cases.
• Creating reusable, scalable and maintainable data products that enable efficient consumption by multiple stakeholders.
• Communicating effectively with technical and non-technical stakeholders to translate complex requirements into practical engineering solutions.
• Producing clear and comprehensive technical documentation covering data pipelines, transformation logic, architecture and operational processes.
• Supporting the design, scoping and implementation of data flows across the wider healthcare data ecosystem.
• Leveraging cloud and big data technologies, including Azure, Databricks, Spark and Data Lakes, to deliver high-performing data solutions.
• Adopting and promoting data engineering best practices, healthcare data standards and continuous improvement across the team.
• Keeping abreast of emerging trends, technologies and standards within healthcare, research and data engineering to drive innovation and excellence.

• Perform exploratory data analysis on a range of complex business problems.
• Interpret business processes into data requirements.
• Define and enforce key data attribute governance strategies.
• Act as geographical data leader, reporting directly to the Data Architecture Manager.
• Collaborate on data team initiatives impacting Ontic’s global strategic pillars.
• Administer and maintain the Snowflake Data Warehouse.
• Devise and maintain data engineering development standards in line with best practice.
• Contribute to data pipelines that clean, transform and aggregate data from disparate data sources into reporting data stores.
• Provide input on the company data strategy by suggesting and proving new technologies and innovations.
• Contributing to the translation of data into valuable insights that inform data driven decisions.
• Develop dashboards and reports to present data insights to stakeholders around the business.
• Support end-user technical questions and assist in data education.
• Host on-site data quality clinics.
• Participate in and audit data governance activities.

• Lead the modernisation and transformation of Newsquest's data platform and supporting architecture, creating a scalable, secure and sustainable foundation for future growth.
• Define and deliver a roadmap for data migration, integration, governance, reporting and analytics capabilities.
• Lead the implementation of a modern data platform, including the development of a data warehouse, data lake, lakehouse or equivalent cloud-based solution.
• Work with stakeholders across the business to understand requirements and translate them into practical data solutions that deliver measurable business value.
• Collaborate closely with IT teams, data engineers, system owners and third-party suppliers to ensure successful project delivery.
• Manage programme plans, budgets, risks, dependencies and resources across multiple workstreams.
• Establish and embed robust data governance, ownership and quality standards across the organisation, increasing confidence and trust in business data.
• Lead and support internal data engineering resources, ensuring successful delivery of platform objectives.
• Drive platform adoption across the business, improving accessibility to trusted data and reducing reliance on manual processes.
• Oversee post-implementation stabilisation, optimisation and continuous improvement activities, ensuring the platform continues to evolve alongside business requirements.
• Identify future opportunities to enhance reporting, analytics, automation and data-driven decision-making.

Data Platform Lead

• Define target-state finance data architectures for clients
• Establish finance data modelling standards
• Design chart-of-accounts, dimensional, and master-data structures
• Map and integrate finance source systems into governed, analytics-ready models
• Design finance data foundations for applied AI/ML
• Set patterns for secure, compliant finance data platforms
• Provide architectural direction for cloud data platforms
• Define the data lineage and controls that support responsible production AI
• Translate ambiguous business and client problems into clear architectural decisions
• Own key technical trade-offs and communicate them clearly

Finance Data Architect

• Develop conceptual, logical, and physical data models for operational, analytical, and regulatory workloads.
• Create and maintain data architecture blueprints and reference architectures covering ingestion, integration, storage, lineage, and consumption.
• Define modelling patterns (3NF, dimensional/star, Data Vault, canonical models) and guide teams on when and how to apply them.
• Optimise physical designs for performance, scalability, cost and resilience across cloud data platforms (e.g., Snowflake, Databricks, Synapse, BigQuery).
• Lead design reviews, ensuring models are accurate, high-quality, and aligned with business capabilities and domain boundaries.
• Define data ownership, stewardship models, and contribute to the governance operating framework.
• Build and maintain data dictionaries, business glossaries, critical data elements (CDEs) and metadata standards.
• Ensure all data solutions align with governance policies, including lineage, retention, access, and security requirements.
• Support governance forums and collaborate with business stewards to drive consistent definitions and controlled change.
• Embed data quality rules and dimensions (completeness, timeliness, accuracy, uniqueness, validity, consistency) across ETL/ELT and streaming pipelines.
• Design data quality metrics, alerts, and monitoring frameworks to surface quality issues to stewards and downstream consumers.
• Partner with engineering teams to implement automated DQ checks, profiling, and remediation patterns.
• Ensure DQ scoring and lineage are built into architecture in a systematic and reusable way.
• Lead architectural discovery to capture business needs, translate them into data requirements, and shape programme/solution scope.
• Manage and communicate architectural decisions with senior business and technical stakeholders.
• Navigate ambiguity and stakeholder nuance, ensuring alignment across product teams, SMEs, vendors, and governance bodies.
• Produce high-quality design documentation and present designs to Architecture Review Boards or equivalent governance forums.
• Oversee the implementation of data architectures and ensure engineering teams follow patterns, standards, and best practices.
• Provide technical guidance on integration design, ingestion patterns, transformation methods, caching, orchestration, and storage optimisation.
• Conduct design assurance throughout delivery, ensuring solutions adhere to non-functional requirements (performance, availability, resilience, cost).
• Coach engineers, analysts, and stewards on modelling, data quality, and governance practices.

Data Engineer jobs in United Kingdom

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Data Architect

Senior Data Engineer

Data Engineer – Tech Lead

Data Engineer

Senior Data Engineer

Junior Data Engineer

Senior Data Engineer - Data Ingestion, Enrichment

Senior Software Engineer – Data

Senior Data Engineer

Product Manager – Core Platform, Data

Data Engineer

Data Migration Consultant

SAP S/4HANA Data Migration Manager

Enterprise Data Architect

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Data Platform Lead

Finance Data Architect

Data Architect – 24 month FTC