Time since we first saw it
Day 96
Up longer than most. Similar jobs usually come down between day 4 and day 77.
From 51 similar postings that came down. Coming down is not always a hire.
Senior AI & Analytics Engineer
Reads like an AI Engineer role: building products on existing AI models.
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingBuild and own scalable data pipelines and analytical datasets using SQL and Python
Define reliable metrics such as retention, conversion and LTV for business decisions
Design and deploy LLM-powered applications and agents using RAG and tool calling
Evaluate and monitor AI solutions in production for accuracy, reliability, cost and safety
Integrate data platforms, APIs and AI components into secure, maintainable services
From the employer’s posting
Build and own scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python.
Translate business and product questions into reliable metrics and decision-support solutions
Design, build, and deploy LLM-powered applications, agents, and analytics workflows using RAG, tool calling, MCP, and modern agent frameworks.
Evaluate, monitor, and continuously improve AI solutions in production, focusing on accuracy, reliability, cost, and safety.
Integrate data platforms, analytics systems, APIs, and AI components into reliable, secure, and maintainable services.
About Pearl
Pearl is AI for professional services at global scale, combining advanced AI with verified human expertise to deliver help that is accurate, accountable, and fast.
In the employer’s words · Read in context
Posting record
Report an errorOpen 3 monthsWe first saw this posting 96 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Aug 9, 202696 days ago
- Recorded sightings
- 35
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Pearl is hiring
1 open job
- No new data jobs since August
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Azure
- BigQuery
- Databricks
- Docker
- Kubernetes
- Power BI
- Snowflake
Where the posting names them
- Analytics Engineering & Business Analytics - Build and own scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python. - Analyze B2C, product, and customer data to identify trends, performance drivers, and actionable insights.
- Relevant Experience: 6+ years of hands-on experience in analytics engineering, data engineering, product/business analytics, applied AI, or a related technical role. - SQL & Python: Strong proficiency in SQL and Python, with experience building and maintaining production-grade data pipelines and analytical datasets beyond exploratory notebooks. - Solution Architecture: Experience designing scalable, end-to-end architectures integrating data platforms, services, APIs, AI components, and user workflows.
- Experience with AI evaluation, monitoring, guardrails, or Responsible AI practices in production. - Built conversational analytics, an analytics copilot, or text-to-SQL / natural-language data experiences. - Worked with semantic layers, governed metrics, or enterprise metric definitions.
- Worked with semantic layers, governed metrics, or enterprise metric definitions. - Google BigQuery, Google Analytics, OpenMetadata, Microsoft Fabric, Power BI, Databricks, Snowflake, Azure AI Foundry, Azure OpenAI, or Claude. - Vector databases, semantic search, or MCP server development.
What you'll build - A governed metrics layer — reliable, reusable definitions for retention, conversion, LTV and other core B2C metrics, built on BigQuery / Fabric with governed definitions in OpenMetadata, that every team and every AI tool draws from. - An analytics copilot — a natural-language interface that lets product managers and business users ask questions of the data and get trustworthy, sourced answers.
- Vector databases, semantic search, or MCP server development. - Docker, Kubernetes, CI/CD, or observability tooling. Our Commitment to an Inclusive Workplace
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job description
About Us
Pearl is AI for professional services at global scale, combining advanced AI with verified human expertise to deliver help that is accurate, accountable, and fast. Since 2003, our network has connected millions of customers with licensed professionals across 196 countries, making real expertise available anytime, anywhere.
Our Values
- Data driven: Start with truth, measure what matters.
- Courageous: Bias to action; run toward hard problems.
- Innovative: Seek novel, elegant solutions.
- Lean: Do more with less. Build, ship, learn fast.
- Humble: Strong opinions, lightly held.
About the Role
Analytics is moving beyond dashboards. We’re building intelligent systems that let analysts and business users explore data, automate analysis, design scalable architectures, and get insights faster and we need a senior engineer to build the data foundations and AI-powered tools that make that possible.
This is a hands-on builder role at the intersection of analytics engineering and applied AI. You'll own the pipelines, datasets, and metrics that the business runs on, and you’ll build the LLM-powered applications, copilots, agents, and natural-language data experiences that sit on top of them. You'll work directly with product, growth, and business teams on real B2C and customer data.
What You’ll Do
- Analytics Engineering & Business Analytics
- Build and own scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python.
- Analyze B2C, product, and customer data to identify trends, performance drivers, and actionable insights.
- Translate business and product questions into reliable metrics and decision-support solutions that help teams measure performance and understand customer behavior.
- AI-Powered Analytics
- Design, build, and deploy LLM-powered applications, agents, and analytics workflows using RAG, tool calling, MCP, and modern agent frameworks.
- Develop reusable AI tools that enable analysts and business users to interact with data, automate analysis, and generate insights.
- Evaluate, monitor, and continuously improve AI solutions in production, focusing on accuracy, reliability, cost, and safety.
- Systems Integration & Production
- Integrate data platforms, analytics systems, APIs, and AI components into reliable, secure, and maintainable services.
- Contribute to the architecture and design of scalable data and AI systems in collaboration with platform and engineering teams.
What you'll build
- A governed metrics layer — reliable, reusable definitions for retention, conversion, LTV and other core B2C metrics, built on BigQuery / Fabric with governed definitions in OpenMetadata, that every team and every AI tool draws from.
- An analytics copilot — a natural-language interface that lets product managers and business users ask questions of the data and get trustworthy, sourced answers.
- Automated analysis workflows — agents that handle recurring analyses (weekly performance reviews, anomaly triage, funnel diagnostics) and surface what matters.
- Reusable AI tooling — skills, knowledge sources, documentation, MCP servers, and ready-to-use scripts that AI tools can call for data extraction, analysis, and visualization, so building the next AI feature is fast and safe.
What We're Looking For
- Relevant Experience: 6+ years of hands-on experience in analytics engineering, data engineering, product/business analytics, applied AI, or a related technical role.
- SQL & Python: Strong proficiency in SQL and Python, with experience building and maintaining production-grade data pipelines and analytical datasets beyond exploratory notebooks.
- Solution Architecture: Experience designing scalable, end-to-end architectures integrating data platforms, services, APIs, AI components, and user workflows.
- Business & Product Analytics: Experience with B2C, product, or customer analytics, including defining and applying metrics such as retention, funnels, cohorts, conversion, and customer lifetime value (LTV) to influence business decisions.
- Production AI Experience: Demonstrated experience personally building and deploying at least one LLM-powered application used by real users, leveraging RAG, tool/function calling, or agents.
- Agent Frameworks: Hands-on experience with at least one orchestration or agent framework, such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, or similar.
- Communication: Ability to explain technical designs and complex concepts clearly to non-technical stakeholders.
Nice To Have
- Experience with AI evaluation, monitoring, guardrails, or Responsible AI practices in production.
- Built conversational analytics, an analytics copilot, or text-to-SQL / natural-language data experiences.
- Worked with semantic layers, governed metrics, or enterprise metric definitions.
- Google BigQuery, Google Analytics, OpenMetadata, Microsoft Fabric, Power BI, Databricks, Snowflake, Azure AI Foundry, Azure OpenAI, or Claude.
- Vector databases, semantic search, or MCP server development.
- Docker, Kubernetes, CI/CD, or observability tooling.
Our Commitment to an Inclusive Workplace
We welcome people from all backgrounds who seek the opportunity to help build a future where professional services are readily available to all. If you have curiosity, passion, and a collaborative spirit, come work with us. Pearl is committed to an inclusive workplace. Pearl is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.
AI Disclosure & Informed Consent
Artificial intelligence (AI) technology may be used during the hiring process to record, transcribe, analyze, and rank interview responses. By submitting your application and participating in the interview process, you acknowledge and consent to the use of AI technology in the hiring process. For more information see our AI Disclosure and Consent Policy.
#LI-Remote
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
India
AI Disclosure & Informed Consent Artificial intelligence (AI) technology may be used during the hiring process to record, transcribe, analyze, and rank interview responses. By submitting your application and participating in the interview process, you acknowledge and consent to the use of AI technology in the hiring process. For more information see our AI Disclosure and Consent Policy. #LI-Remote
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.