Data Operations Analyst

Job Type

Full-time

Description
About Guideline
At , we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.

If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you.

About The Role
The Data Operations Analyst ensures that our published products and market datasets are delivered on time, accurately, and consistently classified. This role owns day-to-day release execution, data QA, and taxonomy maintenance so that client deliverables reflect the highest data quality standards. This role requires written fluency in Simplified Chinese or Mandarin.

Key Responsibilities

• Deliver monthly product and market releases on schedule. Own end-to-end release execution for assigned products/markets, including file intake, data harmonization, QA sign-off, and release communications.
• Produce and maintain monthly QA reporting. Run standardized QA frameworks for assigned datasets, identify data misclassifications, missing values, or structural gaps and document findings. Lead root-cause analysis and partner with cross-functional teams to resolve issues. Publish QA results on recurring cadence and track trends over time.
• Provide transparent status updates. Maintain ongoing communication with the Product team and share regular progress reports on releases, QA outcomes, and open issues, ensuring stakeholders have a clear visibility into risks, timelines, and next steps.
• Harmonize contributor-sourced dimensions Research, input, and validate contributor data and align it to the Guideline hierarchies using external sources as needed.
• Maintain Guideline's proprietary classification taxonomy Update and validate taxonomy structures, document inconsistencies, and ensure approved changes are reflected in downstream products and deliverables.
• Drive workflow and data quality improvements. Contribute to the data management roadmap by proposing and supporting enhancements that reduce manual effort and improve data accuracy and consistency.

Compensation
: We consider a wide range of factors when determining compensation, including relevant experience, education, and skill level.

Benefits
: Guideline offers full-time employees a comprehensive benefits package based on location.

Equal Opportunity Employer:
Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.

Requirements
Required Skills & Experience

• BA/BS required
• Full fluency in English and written fluency in Simplified Chinese (Spanish/French a plus)
• Strong analytical and research skills; comfortable working with large alpha-numeric datasets
• Excellent attention to detail and ability to manage independent workload and deadlines
• Proficiency in Excel (advanced functions: VLOOKUP/XLOOKUP, MATCH, Text-to-Columns, pivot tables)
• Eagerness to learn, improve processes, and build professional skill set
• Familiarity with AI-assisted productivity or data tools (e.g., ChatGPT, Google Gemini, Excel AI, or AI-enabled QA assistants) to streamline research, documentation, and repetitive validation tasks.
• Comfort experimenting with AI tools for classification, taxonomy alignment, or data enrichment to improve accuracy and reduce manual effort.

Preferred Qualifications

• Exposure to the media/advertising industry or a strong interest in learning it
• Exposure to AI-driven data quality or anomaly detection concepts, with an interest in learning how automated checks can support monthly release QA workflows. Experience categorizing or QA'ing large datasets
• Familiarity with enterprise analytics tools (Tableau, PowerBI, Looker, Sisense, Qlik, MicroStrategy, etc.).
• Additional languages are a plus.