Sr. Marketing Analytics Engineer
Equifax, Inc. in, Atlanta, GA, United States
Sr. Marketing Analytics Engineer (Finance)
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.
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
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.
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