Data Engineer

• Design, build and operate scalable data pipelines, data models and end-to-end solution architectures on GCP using BigQuery, Python, dbt, Airflow and Dataform
• Own technical delivery from scoping through production, including design decisions, risk and dependency management, and communicating trade-offs
• Ensure delivery quality through validation, automated testing, observability and data-quality controls
• Diagnose and resolve production issues
• Design and build AI applications, including LLM agents on Google Cloud with Gemini and Google's ADK, from prototype to production
• Use agentic coding tools such as Claude Code or Codex, critically validating their output
• Share effective tools and practices through demos, documentation and reusable patterns
• Collaborate with clients, consultants and data scientists to clarify requirements and translate business needs into pragmatic designs
• Raise engineering standards through design reviews, code reviews, testing practices and documentation

Senior Data Engineer – Data and AI

• Redefine the platform’s architecture across ingestion, processing and delivery
• Build and operate batch and streaming pipelines from databases, APIs, event streams and semi-structured sources
• Implement change data capture and incremental load, including ordering, deletes, replay and slowly changing dimensions
• Build medallion datasets and dimensional models
• Deliver data to Snowflake, Microsoft SQL Server and Databricks
• Apply data contracts, reconciliation and row-level quarantine before publication
• Own the GitHub workflow and CI/CD, including tests, review, environment promotion and deployment as code
• Investigate production data failures
• Translate requirements from product, valuation and client-facing teams into operable pipelines

Senior Data Engineer

• Support a broad set of architectural needs across multiple workstreams
• Design and own the data layer behind GenAI, RAG, and Agentic AI applications
• Ensure enterprise data and knowledge are ingested, prepared, stored, governed, and made available for AI consumption and retrieval
• Own and design data architecture, data modeling, ingestion, and platform modernization for enterprise Azure environments
• Design and implement scalable, metadata/configuration-driven data ingestion and data platform solutions
• Work with structured and genuinely unstructured data across the enterprise platform
• Modernize existing data platforms and ensure implementation-ready architecture

Senior Azure Data Architect – GenAI, RAG, Agentic AI

• Lead assessments of current data landscapes, identifying architectural gaps, data risks, integration issues and opportunities for improvement
• Define target-state enterprise data architectures, data strategies, roadmaps and principles aligned with partner objectives and transformation plans
• Develop conceptual, logical and physical data models, business information models and data domain designs
• Set architecture standards for data products, data flows, integration patterns, metadata and data lifecycle management
• Design scalable cloud data platforms and lakehouse solutions using Microsoft Fabric, OneLake, Azure Data Factory, Azure Databricks, Azure Synapse and related Azure services
• Define batch and real-time data ingestion, transformation, storage, integration and consumption approaches
• Guide the design of data products, pipelines and analytics solutions, including schema evolution, data validation and data quality
• Provide architectural assurance for data migration, legacy modernisation and transitions to modern cloud services
• Design and embed data governance frameworks covering ownership, classification, lineage, metadata, data quality and retention
• Apply Microsoft Purview and related tools for cataloguing, discovery, governance and compliance
• Ensure architecture addresses data protection, access control, privacy, security and regulatory requirements
• Define standards for secure and supportable platform operations, including monitoring, resilience, capacity and cost management
• Lead discovery, architecture workshops and design forums with senior business and technical stakeholders
• Translate technical complexity into recommendations, decision papers and roadmaps
• Collaborate with programme, product, integration, engineering and data teams to make architectural decisions actionable
• Develop PACT's data architecture methods, reusable assets, standards and propositions
• Coach consultants and technical colleagues in data architecture, governance and platform design
• Contribute data architecture expertise to proposals, bids and partner conversations
• Understand assignment scope, value, priorities and delivery commitments
• Conduct commercial conversations with stakeholders and identify opportunities, risks and delivery implications

Senior Data Architect

• Support new and existing customers with onboarding of IP data
• Perform data mapping and manage complex datasets
• Migrate customer data into the preferred format
• Coordinate with engineers and personnel to develop and implement a CSV importer process
• Coordinate and receive inputs from customers and internal stakeholders
• Maintain accurate customer records
• Resolve customer issues directly or involve internal resources to ensure timely resolution
• Stay current with system changes and updates
• Work with customer success, product, and other internal team leads to improve customer experience
• Manage client onboarding from initial handoff through finalization
• Conduct client meetings and prepare data analysis

Data Migration and Onboarding Specialist

• Lead the development of the modern data platform across Microsoft Fabric, Azure Databricks and Power BI
• Design scalable data pipelines and solutions
• Drive data governance, quality and security standards
• Manage and develop a small team
• Work closely with stakeholders across the business
• Support the future analytics and AI roadmap
• Shape data strategy and build scalable, future-ready solutions supporting reporting, analytics and emerging AI initiatives

Head of Data Engineering

• Design and build data pipelines and platform capabilities supporting AI applications, knowledge systems, and AI workflows
• Build and maintain ingestion, transformation, chunking, embeddings, retrieval, vector search, metadata, and knowledge-management pipelines
• Develop secure, reusable access to enterprise data and systems through APIs, MCP, tool calling, and similar patterns
• Build reusable services, libraries, frameworks, and developer tooling for productionizing AI capabilities
• Apply data modeling, data quality, lineage, access, reliability, and governance practices to AI data and systems
• Establish patterns for AI deployment, observability, evaluation, and lifecycle management
• Partner with Engineering, Product, IT, and stakeholders to move AI use cases from prototype to scalable production
• Ensure AI platform capabilities meet security, privacy, access-control, and data-governance requirements
• Evaluate emerging AI and data technologies for potential value to Oyster

• Develop and maintain the enterprise data architecture roadmap aligned with business objectives.
• Design logical and physical data models supporting current and future enterprise applications.
• Define standards, principles, and best practices for enterprise data management.
• Create and maintain data dictionaries, metadata repositories, and documentation.
• Establish scalable architectures supporting ERP, PLM including Manufacturing Systems, and reporting platforms.
• Collaborate with business and technical stakeholders to ensure data structures support operational and strategic requirements.
• Lead the organisation's Data Governance framework and strategy.
• Establish policies, standards, controls, and procedures governing enterprise data.
• Define ownership, stewardship, and accountability models for critical business data.
• Develop data quality metrics and governance KPIs.
• Support compliance requirements related to data security, privacy, retention, and auditability.
• Administer, monitor, maintain, and optimise enterprise database environments.
• Ensure high availability, performance tuning, backup, recovery, and disaster recovery planning.
• Implement database security controls and access management.
• Manage database upgrades, patching, and lifecycle maintenance activities.
• Troubleshoot and resolve database performance and integrity issues.
• Monitor database capacity planning and growth forecasting.
• Lead enterprise data quality improvement initiatives, including data cleansing, validation, remediation, and Master Data Management activities.
• Lead data migration workstreams, including discovery, profiling, mapping, transformation, validation, reconciliation, testing, cutover, and post-go-live stabilisation.
• Oversee migration into PLM, ERP, engineering repositories, manufacturing systems, business systems, and reporting and analytics platforms.
• Design and support integrations between ERP, PLM, CRM, Manufacturing, Quality, and reporting systems.
• Define data exchange standards and interface requirements.
• Support API, ETL, middleware, and data integration initiatives.
• Enable reliable reporting and analytics capabilities and maintain trusted data sources and reporting datasets.
• Work closely with Engineering, Operations, Supply Chain, Finance, Quality, Manufacturing, and IT teams.
• Serve as the primary data subject matter expert during business transformation projects.
• Provide guidance on data-related risks, dependencies, and opportunities.
• Demonstrate commitment to workplace health and safety, report hazards and incidents, and participate in required training.

Data Architect, DBA

• Design, build and operate secure data ingestion and transformation pipelines across internal, external and customer data sources
• Build data workflows spanning cloud, private-cloud, on-premise and datacentre environments
• Develop governed datasets for machine learning and quantum workloads, defining data contracts, versions, quality rules, entitlements, lineage and retention
• Create robust training, validation, calibration and untouched test datasets for credible ML evaluation
• Transform raw customer data into QPU-permitted features or latent representations and return outputs into governed synthetic datasets
• Monitor and optimise pipeline reliability, throughput, latency, storage utilisation and operational cost
• Build automated testing and CI/CD practices for data pipelines and infrastructure
• Partner across engineering teams to deliver reliable data foundations for customer-facing products
• Collaborate across Software, Platform, Security, Machine Learning and Infrastructure teams

• Manage and optimize PostgreSQL, SQL Server, and Oracle databases for performance, security, and availability
• Design and maintain database schemas, indexes, and partitions for scalability and efficiency
• Perform database monitoring, backup, recovery, and disaster recovery planning
• Implement access control, encryption, auditing, and other security best practices
• Troubleshoot database performance issues and optimize query performance
• Ensure compliance with data governance, security policies, and industry standards
• Participate in daily standups and prioritize tasks according to business needs
• Translate business requirements into actionable data engineering tasks
• Collaborate with cross-functional teams to align Power BI and data solutions with business objectives
• Mentor team members and drive best practices in data management and reporting
• Evaluate and apply AI and automation to improve delivery speed, testing, reliability, and operational efficiency

Data Engineer jobs in United Kingdom

Senior Data Engineer – Data and AI

Senior Data Engineer

Senior Azure Data Architect – GenAI, RAG, Agentic AI

Senior Data Architect

Data Migration and Onboarding Specialist

Head of Data Engineering

Senior Data Engineer

Data Architect, DBA

Data Engineer

Senior Data Engineer, PostgreSQL DBA