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Job DescriptionMaintenance EngineerWare£31.71 per hourOnsite | Flexible Working Hours | Approx. 45 Hours Per Day Are you an experienced Maintenance Engineer or Facilities professional looking for a flexible contract opportunity in Ware? Were looking for a hardworking and reliable Maintenance Engineer to work onsite, supporting the day-to-day maintenance and facilities requirements of the site. This is a hands-on role that will involve identifying and checking maintenance issues, liaising with CBRE to arrange callouts, and ensuring any facilities-related problems are dealt with efficiently. The role is starting ASAP, with flexibility around working hours and an expectation of approximately 45 hours onsite per day. The RoleAs a Maintenance Engineer, you will be responsible for supporting the smooth running of the site and coordinating maintenance requirements. Youll act as a key point of contact between the site and CBRE, regularly communicating by phone and email to arrange maintenance callouts and follow up on outstanding issues. You will also carry out onsite checks, coordinate waste collections and assist with general facilities duties where required. Key responsibilities include: Liaising with CBRE to arrange maintenance callouts and site visits Communicating regularly with CBRE via email and telephone Conducting onsite checks to identify and assess maintenance issues Following up on outstanding maintenance and facilities requirements Coordinating and arranging the collection of waste Supporting the general upkeep and smooth operation of the site Keeping relevant parties updated on maintenance issues and progress Carrying out general facilities and building maintenance duties as required Undertaking some manual handling and heavy lifting when required Working independently and managing day-to-day maintenance priorities About You To be successful in this position, you will have: Previous experience within maintenance, facilities administration or building maintenance Experience coordinating or dealing with maintenance issues and contractors Good organisational skills and the ability to manage multiple tasks Strong communication skills, both over the phone and via email Good working knowledge of Microsoft Office Experience using Google Workspace/Google Suite would be advantageous A proactive and hardworking approach The ability to work independently while keeping relevant stakeholders updated A practical approach to identifying and resolving onsite issues Flexibility around working hours Comfortable undertaking some manual handling/heavy lifting when required? #J-18808-Ljbffr

Role: AI Architect Reports to: Chief AI Officer (line management) Location in structure: AI/Data architecture area — embedded within the delivery area it serves, line-managing centrally to the Chief AI Officer Experience: Senior — 6+ years in software, data, or platform engineering/architecture, with meaningful hands-on time on production AI systems, not just pilots Direct reports: None. Influence comes from judgement and presence in the right conversations, not headcount. AI decisions at Citation now cut across Product, Engineering, Security, Infrastructure, and the Business simultaneously — model selection, data architecture, cost, and risk are no longer separable concerns. Without a dedicated architectural owner, each initiative makes these calls independently: patterns diverge, risk goes unspotted until it's expensive, and nobody owns the AI-specific decisions that don't belong wholly to any one function. This isn't a hypothetical gap. Much of this work is already happening informally inside Citation's AI delivery — reviewing partner Statements of Work, governing what goes through Code Factory, acting as the practical architectural voice on live builds. This role formalises that into a mandate with the standing and scope it needs. The AI Architect is Citation's dedicated architect for the AI/Data area, sitting alongside the architects covering Human Resources, Business Systems, Health & Safety, eLearning, Verification, Certification, and Atlas Platform: embedded in the delivery area it serves day to day, but line-managing centrally to the Chief AI Officer so its calls hold across the business, not just the team it happens to sit nearest to. The architecture hub owns target-state and standards across the whole architecture function; this role owns the AI-specific application of it, escalating decisions with consequences beyond AI/Data to the Architecture Review Board rather than deciding them alone. The clearest sign this role is working: AI initiatives at Citation start well and stay on track architecturally. In practice that means: • Established patterns are the default starting point for new builds, not something teams discover after the fact, • Design questions are resolved before Engineering starts building, not during or after, • Third-party Statements of Work are assessed architecturally before they're signed, • Model and hosting choices are made against a documented decision framework, not habit or vendor pressure, • Token spend and cost-per-outcome are tracked and explainable, not a surprise on the invoice, • Security is involved in every significant initiative from the start, not introduced at the end, • Leadership has a current, accurate view of AI architectural risk and direction, with no significant surprises Architectural standards and patterns Own the design patterns Citation builds its AI systems to: retrieval and grounding approaches for systems that need Citation's own knowledge rather than a model's general training, agent orchestration patterns and tool-calling conventions for multi-step and multi-agent work, prompt construction and guardrail design, and the routing logic that decides which model handles which step. Keep these current as the landscape moves, and write decisions down in a form Engineering and Product can actually build against — architecture decision records, not a slide deck. Align standards to Citation's five-layer AI platform architecture. Hold a documented, evidence-based framework for choosing between closed frontier models accessed through a provider's API (Anthropic, OpenAI, Google) and open-weight models run on Citation's own infrastructure (Llama, Mistral, Qwen and similar) — and apply it per workload, not as a single blanket choice. The framework should weigh: • Task complexity — complex reasoning and long‑horizon agentic work generally favour frontier closed models; simpler classification, extraction, and templated generation are often better served by smaller open-weight models at a fraction of the cost, • Volume and cost at scale — at high token volumes, self‑hosting open-weight models can cross into materially cheaper territory; this role owns the analysis of where that break‑even sits for Citation's actual workloads, not a generic industry number, • Data sensitivity and residency — for HR, employment, and compliance data, self‑hosting open-weight models remove a class of third‑party data‑sharing risk that closed providers (even with strong contractual terms) don't fully eliminate, • Fine‑tuning and customisation — where an open-weight model fine‑tuned on Citation's own data would outperform a general‑purpose model at lower running cost, that's a build case this role should be able to make with evidence, not intuition, • Latency, licensing, and total cost of ownership — including the practical overhead of running and maintaining your own models, not just the sticker price of tokens Understand and actively manage the unit economics of every AI solution recommended, including: • Modelling likely token cost at design time, before a system is built, so the business knows what it's signing up for, • Specifying model routing (cheaper models for simpler steps, frontier models reserved for what actually needs them), prompt and context engineering for efficiency, and caching where appropriate, • Recognising that agentic and multi‑agent patterns can multiply token consumption several‑fold over a single well‑scoped call, and that orchestration‑pattern choice is itself a cost decision, • Setting up the observability to track cost‑per‑outcome and cache performance over time, not just the total spend line Assess whether the data behind any AI initiative is structured, accessible, and reliable enough to support the intended behaviour — across Citation's Salesforce, Atlas, Snowflake, and integration layers — and flag readiness issues before they become delivery blockers. Hold the approval path for new AI architectural patterns and material departures from existing standards. Attend initiative design conversations early enough to shape them, not just review them, and sign off on the AI design elements of partner Statements of Work. Define scope and success criteria for AI spikes and proofs of concept before they start, validate vendor and partner capability claims before they're embedded in a committed design, and make sure proof‑of‑concept outputs are evaluated against real delivery constraints — not vendor demo conditions. Act as the internal architectural counterpart to Citation's delivery partners, the way any enterprise architecture function holds its critical vendors to a defined standard: review proposals and Statements of Work before commitments are made, run design reviews during delivery, and give partners a well‑defined target with technical challenge where their decisions need scrutiny. Set the standard for how AI systems are monitored and evaluated once live — offline and online evaluation, drift detection, guardrails — and own the response framework for AI incidents in production, alongside the cost governance described above. Bring Security in as #J-18808-Ljbffr

Good Ofsted | Loyalty bonus | 5* Google reviews | £500 referral bonus Join a tight nit disabilities team which is made up of PERM staff with low turnover in recent times.The council in mind are interviewing ASAP with immediate starts.Benefits of working with Leaders in Care as a child welfare Soci

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Explore your options and find your next move.

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Explore your options and find your next move.

Explore your options and find your next move.

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We sort google data analyst jobs with better pay, shorter commutes and sooner starts in mind.

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We sort google software engineer jobs with better pay, shorter commutes and sooner starts in mind.

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We sort google data analyst jobs with better pay, shorter commutes and sooner starts in mind.

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At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity – it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well‑being. Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full‑time, long‑term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally. Join one of the highest‑rated workplaces on Glassdoor and experience what it means to thrive in a truly remote‑first world. As a Data Engineer, you will help bring the organisation’s data function in‑house, taking ownership of an existing data warehouse from a third‑party provider following a structured handover. The role is approximately 90% data engineering and 10% reporting support and occasional wider IT tasks, with no application or UI development. You will work closely with the Data Analyst and directly with data owners across the business, including operations and finance, to understand end‑to‑end business processes and turn business questions into reliable data solutions. You will also communicate findings to non‑technical stakeholders, including the CEO and directors. This is a permanent, full‑time role. For the global route, it is a full‑time engagement through Smart Working, working UK hours. • Take over the existing data warehouse from the current third‑party data provider during the handover period and then run it in‑house, • Maintain and improve the pipelines moving ERP data into the data warehouse so that data lands reliably and on time, • Write, maintain and extend data models across staging, business logic, marts and reporting tables that feed the reporting layer, • Read and understand the existing codebase before building new models, • Implement and monitor data quality tests, deploying only once tests pass, • Use version control consistently with disciplined commits, clear messages and branching, • Answer business questions using data and explain findings clearly to non‑technical colleagues, including the CEO and directors, • Co‑own technical documentation, core business definitions and the report library., • Cross‑cover with the Data Analyst on reporting during holidays and absence, • Pick up occasional wider IT tasks, with training provided., • Work collaboratively with the Data Analyst and speak directly with data owners across the business, • Manage your own workload and own outcomes, • Maintain a detail‑oriented approach to data accuracy, testing and documentation while understanding the end‑to‑end business processes behind the data and asking “why before how”, • Work flexibly within firm guardrails, with version‑control discipline and test‑before‑deploy practices being non‑negotiable, • Frame and answer business questions rather than simply executing tickets, • Learn quickly during the handover while maintaining a thorough approach focused on pipeline reliability, data quality and business adoption of reports, • 5+ years of data engineering experience for the global Senior route, • Advanced SQL skills, including joins, CTEs, window functions, aggregations and debugging complex queries, with 5+ years of experience for the global route, • 3+ years of commercial, hands‑on Google BigQuery experience for the global route, with production BigQuery experience required, • 3+ years of production dbt experience for the global route, including staging layers, business logic models, marts and tests, • 1+ year of experience with Git/GitHub, including regular commits, clear commit messages and branching. GitLab or Bitbucket are acceptable substitutes, • 1+ year of experience with data warehouse concepts, including ELT, layered modelling and dimensional modelling, • Ability to communicate effectively with non‑technical stakeholders, including CEO and director‑level stakeholders., • Experience with data pipelines and managed connectors for ingesting ERP data into BigQuery. Relevant approaches include Fivetran, Airbyte, Stitch, Azure Data Factory or custom Python ingestion, • Experience with data quality testing and safe deployment, ensuring tests pass before deployment. Relevant approaches include dbt tests, Great Expectations or CI checks., • Full overlap with UK business hours, including the GMT/BST change, • Basic Power BI experience for reporting and cross‑cover with the Data Analyst. Looker, Tableau or other BI tools are acceptable alternatives, • Basic Python skills for simple scripts and automation, • Basic experience with Oracle NetSuite ERP data; experience with other ERP data such as Dynamics, SAP or Sage may be considered, • Awareness of and interest in AI and emerging technology., • Experience owning a pipeline or set of models end to end We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr

Seeking an experienced Marketing Manager to create and execute data-driven marketing campaigns, focusing on lead generation (MQLs) across various channels like Google Ads and LinkedIn, and analyzing campaign performance to optimize ROI.