Marketing Technology Engineer

BUILD A SAFER WORLD.
TRM Labs provides AI-powered intelligence solutions that help public and private sector agencies investigate and disrupt crime. TRM's platforms enable investigators to trace illicit activity, build cases, and construct operating pictures of threat networks. Leading agencies and businesses worldwide rely on TRM to make the world safer and more secure.
Marketing Operations owns the systems the marketing organization runs on: the MarTech stack, the data that describes our funnel, and increasingly the AI agents that do the work. The Marketing Technology Engineer owns the automation and AI layer of that stack — building the agents and automations that remove manual work from the team, and making sure they keep running and keep producing output we can trust. This is a technical role inside a marketing function, and the marketing half is not optional.
The impact you'll have here:
Own the operating layer for marketing's agent fleet end to end — provisioning, output verification, monitoring, alerting, and incident response. Nothing counts as shipped until it has run for real and its output has been checked.
Design and build agentic processes, skills, and automations that remove manual work from the team, and decide — with marketing judgment — which are worth building at all.
Instrument what we build so usage, output, and value are visible to leadership, and so agents improve from feedback rather than staying frozen at launch.
Consolidate how core marketing concepts are defined across tools and agents — territory, lead scoring stages, MQL and MQA, pipeline stages, targets — so automations and dashboards cannot quietly disagree, including across two product lines.
Own the MarTech stack — configuration, administration, and integration — and build in a way the campaign execution team can extend without engineering support for routine work.
What we're looking for:
Prior marketing or marketing operations experience — required, not preferred. You already understand funnel and attribution logic, lead scoring, send-cadence and deliverability norms, and list hygiene and consent rules. This role has no downstream reviewer for marketing correctness.
You have personally operated something unattended. A pipeline, integration, or agent that ran on its own, broke without telling you, and taught you to verify output rather than trust a green status. Scale matters less than the scar tissue.
A track record of building production automation inside a GTM function — Clay, Zapier, Workato, n8n, or similar — where what you built was depended on by other people and you were the one who owned it.
Evidence you have moved from no-code stitching into code and repository practice: version control, code review, CI, testing, and deploying into a shared repo rather than a personal account.
Technical baseline: working proficiency in SQL and Python, solid REST API fundamentals, familiarity with secrets management for credentials and API keys, enough data-warehouse fluency (for example BigQuery) to query and reason about data models, and hands‑on administration of HubSpot and Salesforce.
Nice to have:
Direct experience with Claude, or with n8n.
Experience building multi-step enrichment waterfalls in Clay or similar that others depend on.
Exposure to observability or data‑quality practices — monitoring, alerting, freshness and anomaly checks — in any prior role.
Experience supporting more than one product line, where the same metric has to be correct in two motions at once.
About the Team:
Marketing Operations is a distributed, not distant team that owns the systems the marketing organization runs on.
The work is intellectually difficult because the same metric has to stay correct across two product lines and multiple tools and agents at once.
Ownership is high: this role makes consequential marketing decisions without a downstream reviewer.
THE FOLLOWING REPRESENTS THE EXPECTED RANGE OF COMPENSATION FOR THIS ROLE:
Individual pay is determined by skills, qualifications, experience, and location. The compensation details listed in this posting reflect the US base salary only.
The estimated base salary range for this role is $150,000–$190,000 USD.
Additionally, this role may be eligible to participate in TRM’s equity plan.
Please note – we factor in the different costs for geographies outside the United States
AI FLUENCY AT TRMAI fluency is a baseline expectation at TRM.
We believe AI meaningfully changes how top performers operate. We expect every team member to use AI to accelerate and reimagine their craft, not just automate surface tasks.
At TRM, AI fluency means you are among the top 10 percent of operators in your function in how you apply AI to:
Accelerate repeatable workflows
Structure and solve problems
Improve output quality
Increase speed and leverage
You will be evaluated on applied AI fluency during the interview process.
LIFE AT TRMWe are building a safer world. That promise shows up in how we work every day.
TRM moves FAST. We are a high-velocity, high-ownership team that expects clarity, follow-through, and impact. People who thrive here are energized by hard problems, experimentation, and continuous feedback. If something takes months elsewhere, it will ship here in days.
Our work sits at the intersection of AI, national security, and fighting crime. The problems are complex, the stakes are real, and the environment evolves quickly. The pace and intensity of the work reflect the importance of the mission. As a result, the way we operate requires a high level of ownership, adaptability, collaboration, and creative problem‑solving.
At TRM, you should expect:
Priorities and targets to change quickly
Operating with a high degree of ambiguity
A high level of personal ownership and accountability
Close collaboration across teams and functions
Frequent, high‑touch communication
Creative problem‑solving and out‑of‑the‑box thinking
A pace that rewards urgency, adaptability, and outcomes
This environment is energizing for people who enjoy building, solving hard problems, and making progress in situations that are not always fully defined. It also requires thriving in ambiguity, adjusting course as new information e
#J-18808-Ljbffr