The Challenge
Equifax is building the next generation of marketing intelligence-moving beyond dashboards to conversational AI agents that let your CMO query complex customer datasets like chatting in Slack. You'll architect the data layer, ML pipelines, and agentic tools that turn raw marketing data into actionable insights at scale.
Your Mission
Audit and unify marketing data across Salesforce, Google Ads, Demandbase, LinkedIn, and Snowflake; build the 'Golden Record' schema with complex CTEs and window functions
Design and deploy 3-4 high-impact Tableau dashboards tracking B2B metrics (pipeline velocity, CAC, churn risk) optimized for performance on large datasets
Stand up initial LLM agent (LangChain or CrewAI) that translates natural language queries into SQL and returns results-test with CMO's top 5 recurring questions
Establish data governance framework: document data lineage, implement validation rules, and define confidence thresholds for model outputs to prevent 'confidently wrong' answers
Deploy production ML models for lead scoring and churn prediction (Python/Scikit-learn/XGBoost) with weekly retraining; track lift vs. baseline
Build semantic layer that enables AI to understand marketing taxonomy (e.g., 'spend' maps to correct currency, filters, and attribution logic without manual intervention)
Expand agentic tools to answer 80% of CMO's recurring questions without human intervention; measure success by autonomy rate, not report count
Architect ELT pipelines (dbt + Airflow or Fivetran) for real-time data ingestion from 5+ marketing APIs; achieve <2hr latency for dashboard updates and model retraining
KPIs You'll Own
Model Prediction Accuracy
Lead scoring and churn model AUC-ROC; target 0.75+ on holdout test set
Agentic Query Autonomy Rate
Percentage of marketing questions answered by AI agents without human escalation; target 80%+
Data Freshness SLA
Time from data event to availability in warehouse and dashboard; target <2 hours for all critical marketing datasets
Dashboard Query Performance
P95 query latency on Tableau workbooks; target <5 seconds for deep-dive exploration queries
ETL Success Rate
Percentage of scheduled data pipelines that complete without errors; target 99.5%+
Tools & Stack
Your Team
Your Manager
Not specified; likely VP of Marketing Analytics or Chief Marketing Technologist
Current Team
Not specified; likely data engineers, analytics engineers, and data scientists
New role or backfill not specified
The Package
Salary
$140K-$180K
Remote
On-site: 3 days/week (Tuesday, Wednesday, Thursday) in Atlanta office; flexible hybrid 3/2+2 framework
Benefits & Perks
Company Intelligence
Equifax is a global data, analytics, and technology company serving B2B and B2C customers with credit intelligence, fraud prevention, and identity verification. The company processes massive datasets and is investing heavily in AI-driven marketing intelligence for its enterprise clients.
Funding
Public company (EFX ticker)
Customers
Enterprise B2B and B2C financial services, lenders, retailers
Culture
Enterprise-focused, data-driven, compliance-heavy; moving toward modern data stack (Snowflake, dbt) and AI innovation
Is This Role For You?
- You've shipped ML models (lead scoring, churn, LTV prediction) in production and can speak to lift, not just accuracy
- You can write complex SQL from memory-window functions, recursive CTEs, query optimization-and care deeply about performance on 100M+ row datasets
- You've hands-on experience building LLM agents (LangChain, LlamaIndex, CrewAI) and understand prompt engineering, semantic layers, and retrieval-augmented generation
- You think beyond dashboards: your success metric is 'questions answered autonomously,' not 'reports built'
- You're comfortable in a large, regulated enterprise where data governance and model explainability matter as much as raw accuracy
- You've only built dashboards or BI reports; this role requires deep ML, LLM, and data engineering chops, not visualization polish
- You're not fluent in SQL or Python at a senior level-this is hands-on architecture and coding, not a management position
- You need 100% remote or can't commit to 3 days/week in Atlanta; Equifax's 3/2+2 framework is non-negotiable
- You see 'agentic AI' as hype; this role requires genuine belief that conversational intelligence is the next generation of analytics, not a gimmick
Interview Process
Screening call
30 min with recruiter; focus on SQL depth, ML experience, and LLM familiarity
Technical assessment
Take-home or live coding challenge: write complex SQL queries, optimize for performance, potentially light Python modeling or LLM agent design
Technical deep-dive
2-3 hour conversation with analytics/data engineering lead; walk through your largest ML project, discuss semantic layer design, debate agentic tool architecture
Business case / design exercise
Design a marketing intelligence system for a hypothetical scenario (e.g., 'Build a churn-prediction agent for a SaaS company with Salesforce + Google Ads data')
Executive conversation
30 min with CMO, VP Marketing, or Chief Analytics Officer; discuss how your work impacts business outcomes
Ready when you are
Interested in this role?
Apply now and hear back within days, not weeks.
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About Data & Analytics roles
Data & analytics professionals in marketing transform raw data into actionable insights. They build dashboards, run attribution analysis, design experiments, and help marketing teams make data-driven decisions.