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Quid · 1 day ago

Applied Analytics Engineer - UK

United States

Full-time

Remote

Entry, Mid Level

£70K/yr - £90K/yr

2+ years exp

Quid is a company that combines the power of data, AI, and people to deliver desired outcomes for clients. The Applied Analytics Engineer will build data and AI-driven systems, integrate APIs, and support LLM-based processes to create reliable data flows, ultimately enhancing operational outputs across professional services.

AnalyticsArtificial Intelligence (AI)Data MiningNatural Language ProcessingSoftware

Comp. & Benefits

No H1B

Responsibilities

Build workflow automations in n8n, developing modular, reusable sub workflows and scalable patterns, including structured outputs for Coda briefs and visualisation platforms

Integrate with internal and external APIs, handling authentication, error recovery, retries, rate limits, and tolerant connectivity patterns

Build and refine agentic workflows using LLMs, including guardrails, safe failure modes, and input validation, and experiment with emerging automation and AI frameworks to introduce new patterns and capabilities

Monitor and troubleshoot workflow executions across APIs, agentic behaviour, data transformations, and orchestration layers, implementing effective logging, alerting, and debugging strategies

Break down ambiguous requests into scoped work packages, prototypes, and MVPs

Own workflows end to end, from concept to deployment to ongoing monitoring

Qualification

JavaScriptN8nSQLAPI IntegrationLLM workflowsPythonData handlingVisualisationMonitoringChange managementCuriosityWays of workingCommunicationCollaboration

Required

Experience: 2-3 years building automation, data pipelines, or integration workflows

Core languages: Proficiency in JavaScript for writing expressions and transformations in n8n, with Python a strong plus for ML or analysis workflows. Strong SQL skills (PostgreSQL preferred)

Workflow automation: Hands-on experience with n8n, including modular workflow design and reusable patterns

AI and agentic workflows: Experience building and maintaining LLM based agentic workflows with guardrails, hallucination mitigation and safe failure patterns

Prompt engineering and LLM interaction design: Experience designing and maintaining production grade prompts for LLM driven systems, including clear instruction framing, structured and schema constrained outputs and few shot strategies. Ability to align prompts to business intent, implement guardrails to limit hallucinations, and design prompts that are reliable within multi step automated workflows

API Integration: Experience integrating APIs with robust error handling, authentication, rate limiting, and debugging

Visualisation: Ability to deliver structured outputs in Coda and support lightweight visualisation needs

Data handling: Ability to manipulate and validate structured datasets (JSON, CSV, YAML) with attention to data quality and schema consistency

Engineering foundations: Testing and QA practices, deployment workflows, documentation habits, modularisation, and coding best practices

Observability and reliability: Strong monitoring, logging, alerting, and troubleshooting capabilities for multi-step automation systems

Change management: Experience promoting workflows safely into production and managing production-impacting updates

Ways of working: Requirements gathering, comfort with ambiguity, iterative prototyping, and end-to-end workflow ownership

Communication: Ability to translate technical concepts, risks, and constraints into clear guidance for stakeholders

Preferred

Machine learning fundamentals: Familiarity with exploratory analysis (Python Jupyter, Pandas, Numpy), basic ML workflows, small scale experiments, evaluation metrics (precision, recall, F1, ROC AUC), and approaches for evaluating LLM or agent performance

LLM ecosystem: Exposure to embeddings, vector stores, or retrieval-augmented generation (RAG) patterns

Environment management: Experience working across development, staging, and production environments with safe workflow promotion

Collaboration: Ability to review peer workflows and provide constructive feedback

Curiosity and experimentation: Willingness to explore emerging automation, LLM, and agentic frameworks

Industry context: Experience working with SaaS, analytics, or AI-driven products