Applied Analytics Engineer

Applied Analytics Engineer

$95-110k base + 10% bonus (Depending on Experience)

Remote anywhere in the US.

As an Applied Analytics Engineer at Quid, you will build data and AI-driven systems, integrate APIs, and support LLM-based agentic processes to create reliable and actionable data flows. This role suits someone who enjoys end-to-end automation, collaborating with analytics and engineering teams, and turning ambiguous needs into scalable solutions. Your work will power key insights and operational outputs across professional services (known as our Outcome Engineering Team), enabling faster delivery, higher data quality, and AI-driven prototypes.

You won’t just build workflows – you’ll help shape the next evolution of our data and automation ecosystem and the intellectual property that underpins it.

• 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.

• Experience:
  
  2-3 years building automation
  345 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 (Postgre
  
  SQL 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 agent
  
  Minute 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) iesen 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 endDIV‑to-end workflow ownership.
• Communication:
  Ability to translate technical concepts, risks, and constraints into clear guidance for stakeholders.

• 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‑augmentedritic generation (RAG) patterns.
• Environment management:
  Experience working across development, staging, and production…