Marketing Analytics Engineer

NinjaTrader•Chicago, IL

5d•$135,000 - $185,000•Hybrid

About The Position

The Marketing Analytics team turns NinjaTrader's data into the decisions that grow our business — how we acquire traders, where we invest, and how we measure what works. As our Marketing Analytics Engineer, you'll be the senior technical anchor for how that team models data. You'll own and architect the foundational, shared layer of our dbt models — the well-designed core the rest of the team builds on — and you'll set the AI strategy that makes the whole function faster and smarter. This is a hands-on senior individual contributor role: you lead through architecture, standards, and the quality of what you build.

Requirements

6+ years in analytics engineering or data, including a track record of leading the design of a data-model layer
Deep, hands-on dbt and data-modeling expertise, with experience building a foundational or shared layer that other people built on top of
Demonstrated ability to build with AI, not just use it, such as automated QA/reporting agents, LLM-driven workflows, or internal knowledge tooling
Expert SQL and experience with large, complex datasets and modern data stacks such as BigQuery, Snowflake, Fivetran, or Paradime
Strong grasp of marketing measurement, including attribution logic and metrics like CAC, LTV, and ROAS, with the instinct to keep them MECE
Excellent communication and the ability to set standards others want to follow
Comfort operating in a fast-moving, ambiguous, and scaling environment

Nice To Haves

Marketing mix modeling (MMM), incrementality, and geo-testing experience
Background in fintech, trading, or high-growth consumer tech
Experience setting technical standards for or mentoring other analysts or engineers
Proficiency with BI tooling, Looker preferred

Responsibilities

Architect and own the foundational, MECE core model layer in dbt that the rest of the team builds on top of
Define and evangelize the modeling standards the team builds by, including naming, testing, documentation, and where business logic lives
Own the paid-media attribution model end to end, keeping it accurate and cleanly separated across business units so nothing double-counts
Prevent data leaks and pipeline breakage, catching coverage gaps before they surface late and erode trust in the numbers
Set and execute the team's AI strategy across reporting and data-QA automation, agentic and LLM-driven workflows, and self-serve knowledge tools
Coach the team on building and managing AI agents so an AI-driven way of working becomes standard practice
Partner with the central Data team on changes that touch upstream sources, and support the MarTech hire on the data behind the stack
Scale the model layer cleanly as new business units and geographies come online