What are the responsibilities and job description for the B2B Sales Enablement & Revenue Operations (RevOps) Analyst (Business Data Analyst) position at NLB Services?
Role: B2B Sales Enablement & Revenue Operations (RevOps) Analyst (Business Data Analyst)
Location: Malvern, PA (1st Preference) / Charlotte, NC (2nd Preference) – Hybrid
Contract
ROLE MISSION
The analyst will serve as the crucial "translator" between complex client intent signals (email engagements, website visits, content downloads, event attendance) and the sales force. Utilizing Client's AI ecosystem (driven by Claude LLMs), this individual will design, pilot, and refine "Next-Best-Action" trigger workflows. These triggers will feed directly into Microsoft CRM to guide sales teams on exactly when, why, and how to reach out to corporate clients, directly driving both client retention and net-new business acquisition.
The project will start with 5 months SOW but eventually for a good person this is a long term role
THE IDEAL CANDIDATE PROFILE
Sourcing Target Areas: Looking for senior professionals with background in B2B Marketing Analytics, Sales Enablement, Sales Operations, Growth Operations, or Revenue Operations (RevOps).
Platform Expertise: Prior experience extracting data from Adobe Analytics/Google Analytics and feeding targeted intelligence/signals into Microsoft Dynamics CRM/Salesforce is highly preferred.
AI Native (Good to have): Prior exposure to utilizing Large Language Models (specifically Anthropic’s Claude) to summarize unstructured text, categorize customer intent, or write backend prompts for CRM automation.
We’re looking for a data/business analyst who can turn Signals data into clear, actionable sales insights and help us build and test next-best-action use cases.
- Domain Business Understanding: understands Workplace Solutions business and Sales process (pipeline, RFPs, retention, cross-sell) with the ability to connect data to real business outcomes (not just analysis)
- Strong Translator: Take messy, disparate, and ambiguous data and turn it into clear and simple sales insights. This is not a “report builder”—this is a translator between data and sales
- Bias for action (not perfection): comfortable with ambiguity who is willing to: test, pilot, iterate quickly.
- Comfort with multiple data sources: Able to work across CRM, marketing, external intent, and business intelligence data finding data connections to guide lead evolutions.
- Strong communication: can explain insights simply: “Here’s what the data is telling us and here’s how we should leverage it avoiding overcomplicated analysis.
- Partnership mindset: works closely with product, sales, marketing, data scientist to focus on solving problems together.
- Ownership mentality: takes responsibility for: helping define use cases, driving pilots, showing results while not waiting for direction.
Key Responsibilities:
- Improve data clarity and usability: identify gaps in: data quality, missing fields, confusing signals, and recommend improvements
- Output: Clear list of what data needs to be fixed or enhanced.
- Connect data to real sales use cases: take business intelligence & Signals data (engagement, intent, activity, bombora, product defend/grow, etc.) and map the data so we can translate it into clear use cases for sales
- Output: Clear use case definitions tied to sales behavior
- Build “art of possible” pilots: partner with product sales to design small, testable pilots, focus on proving value quickly (not perfection) Potential example pilots: next best action triggers, lead prioritization signals, client intent summaries
- Output: 2–4 working pilots that show business impact.
- Map data to lead quality: identify which business and Signals data improve lead relevance & context, timing of outreach, conversion.
- Output: Clear mapping of: X data sets improve this type of lead/use case.
- Create client-level insights: pull together data across sources (internal &external Signals) and build a single view of client intent / interest.
- Output: Simple, usable client summaries (what sales should do next)/
- Partner directly with sales: work with sales teams to understand what they need, what’s useful vs. noise, and then iterate based on real feedback
- Output: Insights that create sales opportunities and drive sales effectiveness.
- Support roadmap decisions: help product team decide what data to prioritize, what use cases