AI Enablement Analyst & Builder

ABOUT THE WARREN GROUP

For more than 150 years, The Warren Group has been the Nation’s definitive source for real estate data, property records, and financial media. We serve mortgage lenders, title companies, real estate professionals, and financial institutions with comprehensive property transaction data, registry of deeds intelligence, and market analytics.

We are in an active period of transformation — evolving from a traditional data and publishing business into a modern, AI-enabled data products and solutions company. This role is central to making that transformation real.

ROLE OVERVIEW
This is a builder’s role. The AI Enablement Analyst & Builder is the person who listens, learns, and then goes and makes things. You will talk to people across the organization — in plain business language — to understand how they work, what slows them down, and where AI or automation might help. Then you will go build the solution.

You are not here to write strategy decks or run workshops. You are here to ship working solutions. At the same time, you need enough interpersonal skill and business vocabulary to earn trust from non-technical teammates and draw out the ideas they have not yet articulated. Think: part Business Analyst, part Data Analyst, part AI/Automation Engineer — all in one person.

The right candidate has genuine curiosity, moves quickly from concept to working prototype, communicates with clarity in business terms, and has the technical range to build production-ready solutions without needing hand-holding. If you get energy from solving real problems and shipping things that people actually use, this role is for you.

KEY RESPONSIBILITIES
1. Discovery & Requirements Gathering

You bring the listening skills of a Business Analyst. You create the conditions where colleagues feel comfortable sharing friction points, half-formed ideas, and wishlist items — then you translate those into structured, actionable requirements.

Conduct structured discovery conversations with staff at all levels to surface manual, repetitive, or error-prone work

Document current-state workflows using plain-language process maps, swim-lane diagrams, or simple data flow sketches

Translate raw business feedback into technical requirements — capturing inputs, outputs, logic, edge cases, and success criteria

Prioritize opportunities by ROI, effort, and strategic alignment — communicating trade-offs clearly to the CEO and leadership

Build enough trust with each team that they proactively bring you their next idea

2. Build, Automate & Implement

This is the core of the role. Once a problem is defined and a solution is scoped, you build it — not hand it off.

Design and build AI-powered workflows, automated pipelines, and intelligent tools using Python, APIs, LLMs, and low-code/no-code platforms

Implement automation across business functions: data operations, sales ops, finance, marketing, editorial, and client reporting

Integrate AI capabilities (e.g., LLM-based classification, extraction, summarization, generation) into existing workflows

Connect systems via APIs — internal databases, CRMs, SaaS tools, and data platforms

Deploy, monitor, and iterate on solutions — you own the work until it is stable and adopted

Write clean, maintainable code and document your work so the team can understand and extend it

3. Data Analysis & Operational Reporting

You think in data. You can pull, shape, and analyze data to validate assumptions, measure impact, and surface insights — and you communicate those findings in plain business language.

Query operational databases and data pipelines using SQL to understand data structure, quality, and volume

Build lightweight dashboards and reports that give leadership real-time visibility into operational KPIs and initiative progress

Conduct pre/post analysis to measure the impact of process changes and automation deployments

Identify data quality issues that block automation or distort reporting — and fix them

Support the data and technology teams with field-level validation, deduplication, and quality assurance workflows

4. AI Enablement & Knowledge Sharing

You inspire curiosity — but through doing, not presenting. When you build something that works, you show it to people, explain it simply, and make it approachable. That is what drives adoption.

Demo working solutions to colleagues in non-technical language — focused on what it does for them, not how it works under the hood

Run brief, hands-on sessions to help teams learn and use new tools — not all-day workshops, just targeted skill-building

Document how solutions work in plain language so they are maintainable and extensible by others

Stay current on AI tooling, platforms, and practical use cases relevant to TWG’s operations and data products

Advise the CEO on when a proposed AI use carries legal, IP, or contractual risk — and know when to escalate to counsel

5. AI Risk & Governance Awareness

Develop working knowledge of legal, regulatory, and ethical considerations around AI use in a data and media business

Identify risks related to data privacy, third-party AI platforms, IP, and client contract obligations before deploying solutions

Help establish practical guardrails and usage standards for responsible AI adoption across the organization

Flag issues proactively and engage legal counsel when the risk profile warrants it

EXPERIENCE, SKILLS & QUALIFICATIONS

Required

5–9 years of experience in a builder role — business/data analysis, automation engineering, data engineering, or applied AI/ML

Demonstrated ability to independently build and ship solutions — from scoping through deployment — not just document or recommend

Strong communication skills: able to interview non-technical stakeholders, extract real requirements, and explain technical work in plain business terms

Hands-on Python development: scripting, API integration, data manipulation (pandas, etc.), and workflow automation

Working knowledge of LLMs and AI APIs (OpenAI, Anthropic, Azure OpenAI, or similar) and practical experience building with them

SQL proficiency: ability to query relational databases, understand schemas, and manipulate data independently

Experience with automation platforms (e.g., Make, n8n, Zapier, Power Automate) and low-code integration tools

Comfort in a lean organization where you set your own direction and own your work end-to-end

Preferred

Experience in data-driven organizations — real estate, financial services, or information services a plus

Familiarity with data pipeline concepts: ETL, ingestion workflows, normalization, deduplication, quality assurance

Experience with BI and reporting tools (e.g., Power BI, Tableau, Metabase) for building operational dashboards

Exposure to vector databases, embeddings, or RAG architectures for document and data retrieval applications

Familiarity with CRM systems (HubSpot preferred) and sales/marketing automation workflows

Understanding of legal and compliance considerations related to AI: data privacy, IP, vendor contracts, and model usage terms

Background in process improvement frameworks (Lean, Six Sigma, Agile) — not required, but useful context

Degree in Computer Science, Information Systems, Business Analytics, or a related field — or equivalent demonstrated experience

WHY THE WARREN GROUP

Build things that matter — your work will be visible, used, and felt across the entire organization

Access to rare data — help unlock the value of one of the Nation’s richest real estate and property transaction datasets

Transformation ---------- nt — join at the point where decisions made today define the company’s operational DNA for years ahead

Close collaboration — report directly to the CEO and work alongside every part of the business

Autonomy with support — you will be trusted to lead your own work, with leadership engaged and ready to remove blockers

Hybrid flexibility, competitive compensation, and a team that values execution over process