Sr. Marketing Analytics Engineer

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. What you’ll do 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. What experience you need 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. What could set you apart 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. #J-18808-Ljbffr