Cdc jobs in United Kingdom

Data Engineer – Azure & Snowflake Modernisation (Outside IR35) 6‑month contract | 4 days/week on‑site (City of London) A leading organisation is modernising its data ingestion landscape and looking for a Data Engineer to help migrate legacy ETL workloads into scalable, cloud‑native pipelines on Azure and Snowflake. Responsibilites: • Designing and building robust batch and near‑real‑time ingestion pipelines, • Migrating legacy ETL/ELT into modern Azure + Snowflake patterns, • Implementing incremental loads, CDC, schema evolution, backfills, • Standardising ingestion using reusable frameworks and best practices, • Contributing to a modern data platform built on ADF, Databricks, Synapse Skills: • Strong data engineering background with a focus on data ingestion, • Hands‑on experience with Azure Data Factory, Databricks, Synapse, • Solid SQL and experience with Snowflake, • Proficiency in Python for data processing and automation, • Understanding of data lake/lakehouse patterns, • Exposure to Terraform, Azure DevOps, and orchestration tools (e.g., Dagster) Apply now for more details!

Brickendon Consulting is an award-winning global management and technology consultancy specializing in innovative solutions and transforming complex, highly regulated environments. Founded in 2010, Brickendon has a strong focus on financial services and the public sector, helping organizations navigate and implement significant changes. We have delivered projects for some of the world’s largest firms in financial services, governments, and leading fintechs and startups, including JP Morgan, HSBC, Deutsche Bank and Rolls Royce. We are currently seeking a Integration Architect for one of our clients. ✅Work location: London ✅Work setting: hybrid Key Responsibilities • Produce integration specifications (D11) for each source system, defining connectivity patterns, extraction methods, and data transfer protocols., • Design the DSFS integration architecture (D06b), including Salesforce API usage, AWS RDS connectivity, and MongoDB extraction patterns., • Define CDC configurations for near real-time data feeds, specifying change tracking mechanisms., • Specify Talend job designs for on-premises Oracle source extraction and cloud-native ELT patterns., • Coordinate with the DSFS Integration Specialist on platform-specific technical details. Knowledge and Experience • Minimum 5 years in data integration architecture, covering both batch and real-time patterns., • Experience with Salesforce integration: APIs (REST, Bulk, Streaming), platform events, and data replication patterns., • Knowledge of CDC mechanisms: database log-based CDC, trigger-based CDC, and timestamp-based extraction., • Experience with Talend or equivalent ETL tooling for on-premises source extraction., • Familiarity with MongoDB data extraction and transformation patterns., • Experience designing integration architectures for multi-source estates of 10+ systems., • Experience within the loans or financial services domain., • Familiarity with Oracle database integration patterns (relevant to CLASS and legacy systems)., • Knowledge of AWS integration services: Glue, DMS (Database Migration Service), or Step Functions

Opportunity for Data Modeller / Data Designer • This role empowers you to shape end-to-end data ecosystems—accelerating delivery, enhancing data clarity, strengthening operational resilience, and driving organisations toward a more insight-rich, data-enabled future., • You will define the data blueprints and foundational models that underpin how customers in dynamic, data-intensive industries operate, scale, and innovate., • You will design robust, future-ready data models that enable seamless integration, advanced analytics, and AI-driven decision making across complex digital transformation programmes. Essential Skills & Experience - Data Modeller / Data Designer • Experienced Data Modeller, Data Designer, Data Specialist or similar, • Proven experience delivering conceptual, logical, and physical data models for cloud data platforms, ideally GCP, • Strong hands-on modelling for Big Query (analytical/columnar patterns, denormalization strategy, partitioning & clustering considerations), • Expertise in data modelling approaches: 3NF, dimensional (Kimball), Data Vault, and hybrid patterns for Lakehouse designs, • Maintain versioned model artefacts (ERDs, schema scripts, JSON/YAML specs) and change logs; manage controlled evolution of models., • Ability to translate banking domain requirements (Customer, Accounts, Payments, Credit, Risk, Finance) into scalable canonical models, • Strong understanding of BigQuery performance and cost optimisation impacts driven by modelling choices (query patterns, storage, scan costs), • Experience designing data products for analytics and reporting with trusted definitions (facts, dimensions, SCD, conformed dimensions), • Proficiency with data modelling tools such as ER/Studio, PowerDesigner, ERWin, SQL Developer Data Modeler, or equivalent cloud-native tools. Key Responsibilities - Data Modeller / Data Designer • Define and maintain conceptual, logical, and physical data models that accurately reflect business processes and support analytics, AI/ML, and operational needs., • Translate business requirements into robust data entities, attributes, relationships, and constraints; ensure traceability from requirements to models., • Establish and enforce GDM modelling standards and naming conventions (e.g., normalization, dimensional/star/snowflake patterns, data vault where appropriate)., • Design dimensional models (facts, dimensions, hierarchies, slowly changing dimensions) for BI/analytics and performance at scale., • Create and manage canonical data models and semantic layers to enable consistent metrics and self-service analytics across domains., • Optimise models for performance and cost (partitioning, clustering, indexing, compression, surrogate keys, distribution strategies)., • Drive data integration design across sources (CDC, event streaming, APIs), mapping source-to-target, resolving conflicts, and handling historical changes., • Support AI/ML readiness by modelling features, aggregations, and histories; collaborate on feature stores and model input/output schemas.

The Lead Engineer / Technical Lead will own the end-to-end technical delivery of a data migration and platform modernisation programme in AWS. This role acts as the single technical point of accountability, setting architecture, defining standards, and coordinating delivery across workstreams. While the programme includes data engineering, the focus is heavily on DevOps, infrastructure, and platform engineering, enabling scalable, secure, and automated migration and ingestion of enterprise data. Key Responsibilities • Own and evolve the AWS/CDP architecture for data migration, ingestion, and downstream consumption, • Define and enforce engineering standards (Terraform, CI/CD, pipeline design, naming conventions), • Lead platform engineering and infrastructure delivery (IaC, networking, security, environment setup), • Oversee migration of ~8–10 enterprise databases into AWS using DMS and CDC patterns, • Ensure migration pipelines are scalable, automated, and resilient, • Enable event-driven and batch data pipelines (S3, Lambda, orchestration), • Ensure data lineage, cataloguing, and documentation are captured, • Coordinate backlog prioritisation, sprint planning, and delivery sequencing, • Validate deliverables for quality, performance, security, and operational readiness, • Act as senior technical interface with stakeholders and delivery partners, • Lead knowledge transfer and upskilling of engineers Required Experience & Skills • 5+ years in data/platform engineering, including leadership experience, • Strong AWS experience: S3, Lambda, IAM, VPC/networking, DMS, • Proven experience with Infrastructure-as-Code (Terraform) and CI/CD pipelines, • Experience delivering large-scale data migrations, including CDC approaches, • Strong Python and SQL skills, • Understanding of data platforms (e.g. Snowflake/CDP) and ingestion pipelines, • Exposure to AI/ML enablement frameworks (.e.g AWS SageMaker) and supporting infrastructure for model training, deployment, • Familiarity with modern development and deployment frameworks, • Experience with dbt / ELT patterns (beneficial, not core focus), • Strong stakeholder communication and technical leadership skills

Location: London Company: Timebeat.app Reports to: CTO About Timebeat Timebeat is building next-generation infrastructure for precise, verifiable time and frequency synchronization across distributed systems. Our technology sits at the intersection of hardware, FPGA design, and large-scale systems, enabling customers to measure, guarantee, and reason about time in ways that were not previously possible. As we scale, we are looking for an FPGA engineer who wants to own major technical initiatives, help shape architecture decisions, and grow into a people- and project-leadership role. The Role This role is ideal for a strong FPGA engineer who is ready to take the next step in their career—moving beyond individual contribution into technical leadership, mentoring, and project ownership. You will remain hands-on with FPGA design while also: • Leading FPGA-centric projects end-to-end, • Acting as a technical point of contact across hardware, firmware, and software teams, • Supporting and mentoring other engineers, • Helping define best practices, processes, and technical direction for FPGA development at Timebeat Key Responsibilities Technical Leadership & Delivery • Own the architecture, design, and delivery of FPGA solutions used in production systems, • Translate system-level requirements into FPGA designs and implementation plans, • Drive design reviews, technical decision-making, and trade-off discussions, • Design, implement, and verify FPGA logic using VHDL and/or Verilog/SystemVerilog, • Work with high-speed interfaces, clocks, timing closure, and synchronization logic, • Integrate FPGA designs with embedded software, drivers, and system-level components, • Mentor junior and mid-level engineers through code reviews and technical guidance, • Contribute to hiring and onboarding as the FPGA team grows, • Work closely with software, systems, and product teams to deliver customer-facing features, • Communicate complex technical topics clearly to both technical and non-technical stakeholders, • Strong experience in FPGA design and development in a production environment, • Proficiency in VHDL and/or Verilog/SystemVerilog, • Experience with FPGA toolchains (e.g. Xilinx / AMD, Intel / Altera), • Solid understanding of:, • Clocking and timing constraints, • CDC, synchronization, and deterministic timing, • Debugging FPGA designs on hardware, • Experience owning significant technical components or projects, • Prior experience leading small teams or acting as a technical lead, • Experience with time-sensitive systems, networking, or high-precision timing, • Exposure to embedded Linux, drivers, or low-level software interfaces, • Familiarity with formal verification, simulation frameworks, or CI for FPGA, • An engineer who enjoys both building and leading, • Someone excited by ownership, responsibility, and seeing projects through to production, • A collaborative communicator who raises the bar for those around them, • Work on deep technical problems with real-world impact, • Influence the technical direction of FPGA development from an early stage, • Grow into a leadership role at a fast-moving, highly technical company, • Competitive compensation, flexible working, and strong engineering culture

The Opportunity - (Fully Remote, Perm role) This is a greenfield data platform build. ___ is in the middle of a major AI transformation — a multi‐agent copilot platform (KAI) is already live in production, serving manufacturing clients globally. The AI roadmap is ambitious (agentic AI capabilities, multi‐agent orchestration, voice interfaces), and the next wave of capabilities depends on a strong data engineering foundation. A Databricks lakehouse POC is being evaluated (Unity Catalog, Delta Sharing, PrivateLink to RDS), and the team is assessing the right long‐term analytics platform strategy. This role builds the entire data engineering function from scratch — defining the architecture, tooling, and patterns that power the next generation of AI capabilities. This is an entirely forward‐looking role. While there is an active platform modernisation underway (the engineering team is migrating a .NET platform from SQL Server to PostgreSQL and containerising for Kubernetes), this role’s primary focus is building new data pipelines and analytics infrastructure, not maintaining legacy systems. That said, collaboration with the platform engineering team is important, as the data pipelines need to ingest from both the existing SQL Server estate and the new PostgreSQL databases. Core Technical Requirements • Python — primary language for data pipelines, scripting, and integration with the AI stack. This is a Python‐first team, • SQL (SQL Server + PostgreSQL) — deep experience required across both engines. The platform runs a large‐scale multi‐tenant database estate with per‐client schemas (local, global, corporate). The new platform targets Aurora PostgreSQL, with several modules already migrated. Pipelines must bridge both., • Databricks / Apache Spark — a lakehouse POC is being evaluated with Unity Catalog and Delta Sharing via AWS PrivateLink. Experience with Databricks or equivalent analytics platforms is important for assessing and scaling the right approach, • Apache Kafka — event streaming experience is essential. The platform is evaluating Kafka as the strategic backbone for ordered event logs, consumer group replay, and guaranteed partition ordering as the microservices fleet scales, • ETL/ELT pipeline design — building and maintaining pipelines into a medallion architecture (bronze/silver/gold layers) on Databricks or equivalent analytics platform, • Delta Lake — understanding of lakehouse table formats, time travel, and ACID transaction patterns, • AWS data services — RDS, S3, Lambda, Glue or equivalent. The platform runs entirely on AWS • Linux / CLI proficiency — must be comfortable working AI-Assisted Development Methodology -> Solvace is transitioning towards AI‐assisted development as a core engineering practice. Candidates should demonstrate: • Hands‐on experience with AI coding tools — Claude Code, OpenAI Codex, GitHub Copilot, Cursor, or similar. We’re looking for engineers who have integrated these tools into their professional workflow, not just experimented casually, • Spec‐driven development — ability to write clear technical specifications that can be used to drive both human and AI‐assisted implementation, with strong evaluation criteria and test coverage, • Portfolio evidence — professional projects or side projects that demonstrate AI‐assisted development practices. Contributions to or experimentation with emerging projects like OpenClaw are a strong signal, • Testing and evaluation rigour — experience building robust test suites, automated quality gates, and evaluation frameworks that ensure AI‐assisted code meets production standards Nice-to-Have -> • Apache Flink — for real‐time stream processing as the platform moves from batch to streaming analytics, • Apache Iceberg — experience with open table formats alongside or as an alternative to Delta Lake, • Go or Rust — valued as evidence of strong backend engineering and systems thinking, even though Python is the primary language, • Multi‐tenant data architectures — the platform runs per‐client schemas with tenant isolation requirements. Understanding tenant isolation patterns is a significant advantage, • Terraform / IaC — infrastructure is managed via Terraform; the Databricks POC has been codified in Terraform, • dbt or similar transformation framework, • Manufacturing / industrial data domain experience (sensor data, quality metrics, OEE, SPC), • CDC (Change Data Capture) — the platform is building CDC pipelines for the SQL Server to PostgreSQL transition, • Weaviate or other vector database experience — currently used for semantic search in the AI agent layer Why Join? • Greenfield data platform — the entire data engineering practice needs to be built from scratch. A lakehouse POC is being evaluated but the production architecture is theirs to define — patterns, tooling, and platform decisions from day one, • Direct AI impact — every pipeline they build directly unblocks AI agent capabilities. The KAI copilot is live in production and evolving towards agentic AI — autonomous agents that take actions, orchestrate workflows, and operate across systems. This role provides the data foundation that makes agentic capabilities possible, • Event streaming architecture — opportunity to design and implement a Kafka‐based event backbone, shaping the platform’s real‐time data architecture, • Manufacturing AI — opportunity to work at the intersection of industrial operations and generative AI, a domain with massive untapped potential. Rich, real‐world data: quality inspections, KPIs, maintenance records, safety observations, process parameters across global manufacturing sites, • Small team, high autonomy — the Innovation Hub operates with startup‐level autonomy inside a funded, growing company, • Leadership trajectory — the data function is critical to the platform and will grow. As an early hire, there is an open career path that will grow with the function — earned, not guaranteed

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Your new company Working for a renowned financial services organisation Your new role Seeking a Data Engineer to help design and maintain scalable batch and near‑real‑time ingestion pipelines, modernizing legacy ETL/ELT processes into Azure and Snowflake, and implementing best‑practice patterns such as CDC, incremental loading, schema evolution, and automated ingestion frameworks. They build cloud‑native solutions using Azure Data Factory/Synapse, Databricks/Spark, ADLS Gen2, and Snowflake capabilities including stages, file formats, COPY INTO, and Streams/Tasks to support raw‑to‑curated data modelling. The role involves creating reusable components and Python libraries to accelerate delivery across teams, enforcing data quality through validation, observability, and robust pipeline design, and ensuring strong security, governance, and documentation standards. Collaboration within agile workflows-including CI/CD, code reviews, and iterative planning-is also key to delivering consistent, reliable, and secure data solutions. What you'll need to succeed Strong hands-on data engineering experience, with strong focus on data ingestion Experience building production pipelines using Azure Data Factory, Databricks, Synapse Solid SQL skills and experience working with modern cloud data warehouses, ideally Snowflake Proficiency in Python for data processing, automation, and pipeline utilities Good understanding of data lake/lakehouse concepts and ingestion patterns Infrastructure-as-Code exposure (Terraform) and CI/CD (Azure DevOps) Able to prototype quickly while adhering to Group standards and controls Communicates clearly with business stakeholders and technical teams Familiarity with orchestration frameworks (Dagster) - desirable Energy commodity trading experience is a real advantage What you'll get in return Flexible working options available. What you need to do now If you're interested in this role, click 'apply now' to forward an up-to-date copy of your CV, or call us now. Hays Specialist Recruitment Limited acts as an employment agency for permanent recruitment and employment business for the supply of temporary workers. By applying for this job you accept the T&C's, Privacy Policy and Disclaimers which can be found at (url removed)