Sr. Marketing Analytics Engineer

Equifax•Atlanta, GA

4d•Hybrid

About The Position

Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds, and make a meaningful impact, we want to hear from you. As a Sr. Marketing Analytics Engineer, you will sit at the intersection of data engineering, data science, and generative AI. Your mission is to transform raw marketing data into a conversational asset, enabling the organization to query complex datasets as easily as sending a google chat message. This isn't just a "dashboard" role—it is a technical architect position focused on building the "Marketing Intelligence Layer." You will be responsible for the full lifecycle of data: from complex SQL modeling and predictive ML to the development of agentic tools that translate natural language into executable code and back again.

Requirements

SQL Mastery: 5+ years of experience writing expert-level SQL (window functions, complex CTEs, recursive queries) with a focus on optimizing performance for large-scale marketing datasets.
The "Agentic" Frontier: Proven experience working with Large Language Models (LLMs) and frameworks like LangChain, LlamaIndex, or CrewAI to build autonomous data agents.
Predictive Modeling: Hands-on experience building and deploying Machine Learning models in a production environment (Python/Scikit-learn/XGBoost) specifically for Lead Scoring, Churn.
Tableau Development: Senior-level experience building Tableau workbooks that aren't just pretty, but are architected for performance and deep-dive exploration.
Consistent with Equifax’s 3/2+2 flexible work framework, a willingness to work Tuesday, Wednesday, Thursday in Equifax’s Atlanta-based office

Nice To Haves

Marketing-First ML Logic: A standard engineer builds a model that works in a vacuum. A standout candidate builds a model that accounts for marketing reality. They understand the nuance of "incremental lift" versus "last-click," and they build agentic tools that can explain why a model made a specific recommendation (e.g., "The agent suggests increasing Meta spend because the ML model predicts a 15% higher LTV for this specific segment").
From Dashboarding to "Conversational Intelligence": While others are proud of their Tableau formatting, this person is focused on making the dashboard optional. They stand out because they view their role as building a "Marketing Brain." Their success metric isn't how many reports they built, but how many questions were answered via their agentic tools without human intervention.
The "Zero-Latency" Translator: Most candidates can write SQL, and many can play with LLMs. The standout candidate has mastered the Semantic Layer. They don't just "hook up" an AI to a database; they build the complex metadata layer that ensures when a CMO asks for "spend," the AI knows exactly which filters, currency conversions, and date ranges to apply. They solve the "hallucination" problem with engineering, not just hope.

Responsibilities

Strategic Dashboards: Designing high-impact Tableau visualizations that track B2B marketing metrics.
ML Output Integration: Visualizing the results of predictive models (e.g., showing "Predicted Churn" risk segments).
Data Unification: Building complex joins and CTEs (Common Table Expressions) to create a "Golden Record" of customer behavior across disparate platforms like Salesforce, Google Ads, and Snowflake.
Data Governance: Ensuring data hygiene and accuracy; if the underlying data is messy, the agentic tool will provide "confidently wrong" answers
ETL/ELT Ownership: Orchestrating the flow of marketing data from APIs (Demandbase, Google, LinkedIn) into the data warehouse using tools similar to dbt, Fivetran, or Airflow.

Benefits

We offer comprehensive compensation and healthcare packages, 401k matching, paid time off, and organizational growth potential through our online learning platform with guided career tracks.

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