GTM Data Operations Engineer

MPLC•,

1d•$85,000 - $100,000•Remote

About The Position

MPLC is hiring a GTM Data Operations Engineer to own the data pipeline that sources, enriches, and qualifies US accounts for the Mid-Market and Enterprise segments. The role uses sources such as ZoomInfo, Dun & Bradstreet, SEC/EDGAR, and industry registries, and delivers sales-ready account batches on a consistent weekly cadence. This is an individual contributor role with ownership of the pipeline. The engineer will work to quality standards and playbooks set by the Principal and Senior, while improving those standards and playbooks. The role partners with sales, demand generation, RevOps, and IT to maintain trusted data and address quality issues. The position is remote from anywhere in the US, with occasional paid travel. Base salary is $85,000 to $100,000 per year depending on experience.

Requirements

At least 2 years building GTM data workflows or B2B data pipelines, with demonstrable results such as accounts sourced, improved acceptance rates, or manual steps removed.
Ability to read, write, and debug Python pipeline and web-scraping scripts independently.
Experience with Clay enrichment tables, waterfall logic, and AI agents in production, with attention to credit usage.
Salesforce fluency, including its object model, field mapping, and CRM hygiene.
Experience shipping at least one AI workflow in a GTM context and describing its output.
Ability to collaborate with sales, demand generation, RevOps, and IT.
A quality-focused approach and understanding of what makes data sales-ready.

Nice To Haves

Familiarity with US B2B data sources such as ZoomInfo, Dun & Bradstreet, SEC/EDGAR, or similar.

Responsibilities

Build net-new account detection using Python web scraping and Clay.
Qualify US Mid-Market and Enterprise accounts against the ideal customer profile.
Deliver sales-ready account batches every week.
Manage enrichment logic and re-score existing accounts.
Work with sales and demand generation to close the loop when data quality falls short.
Improve the quality standards and playbooks set by the Principal and Senior.
Maintain a trusted pipeline and explain data logic in plain language.