Sr. Marketing Analytics Engineer
Equifax, Inc., Atlanta, GA, United States
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.
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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