Senior Data Analyst (AdTech | Python | Media Data Operations)

Description: About Guideline At Guideline.ai, 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

We are looking for a Senior Data Analyst who is highly practical, detail-oriented, and strong in Python- and SQL-based data manipulation. This role is hands-on and focused on cleaning, standardizing, and enriching large, messy advertising spend datasets—especially digital data sourced from MediaOcean’s Prisma platform, where key information often lives inside inconsistent free-form text fields. In addition to data transformation and quality work, you’ll apply statistical and machine learning techniques to forecast spend trends, fill data gaps, and classify unstructured text data. You’ll also be expected to stay aware of emerging AI-assisted and generative AI technologies, understanding how they can improve analysis and automation even if you’re not directly building such models. What You’ll Do Data Cleaning, Parsing & Standardization (Core Function)

• Build Python (Pandas/Regex) workflows to extract structure from free-form Prisma text fields containing placements, publishers, formats, devices, tactics, etc.
• Develop rule-based and pattern-based logic to recognize abbreviation variations, naming inconsistencies, and agency-specific formatting habits.
• Create normalization scripts that standardize publisher names, platform types, channels, and other key taxonomies.
• Maintain “classification dictionaries” and pattern libraries that improve accuracy over time.

Data Quality & Operational Support

• Profile incoming agency data to identify common quality issues and propose concrete remediation.
• Design QA checks to detect missing fields, malformed entries, inconsistent dimensional labels, and mismatched spend totals.
• Partner closely with Data Engineering to embed your logic into scheduled pipelines.
• Own recurring operational workflows that support data ingestion, aggregation, and quality monitoring.

Dimensional Enrichment, Modeling & Data Derivation

• Build and maintain predictive models to support media spend forecasting, pricing analysis, and text classification for data tagging and enrichment.
• Apply statistical modeling or regression techniques to impute missing values or infer incomplete spend or performance metrics.
• Derive new fields—such as format groupings, platform classifications, creative types, and targeting attributes—using pattern recognition and model-based inference across text fields.
• Produce structured dimensions from unstructured data to improve downstream reporting.
• Document the logic and assumptions behind model-driven derivations to ensure transparency and reproducibility.

Analysis & Internal Stakeholder Support

• Investigate anomalies, unexpected trends, and data gaps by tracing issues back to source fields.
• Build dashboards that help internal teams observe data quality, volume, deviations, and model-driven insights.
• Provide ad-hoc deep dives into digital spend patterns, forecast accuracy, and classification performance for product, ops, and leadership.

Why This Role Matters Your work directly determines the quality, usability, and credibility of the datasets our company provides to clients and uses internally. This is a core operational and analytical role, focused on making messy agency data usable at scale—while adding new analytical capabilities through modeling, forecasting, and intelligent automation.

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. Some benefits may include, but are not limited to:

• Medical, Dental, Vision, Health Savings Account, Flexible Spending Account
• STD, Life, LTD and AD&D
• 401(k) with a company match program
• Unlimited Paid Time Off (PTO)
• Paid Parental Leave
• Commuter Benefits
• Employee Recognition Program
• Referral Bonus Program

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: What You Bring

• 5+ years of experience as a Data Analyst or Data Operations Analyst.
• Expert-level Python (Pandas, Regex) and SQL.
• Experience extracting meaning from unstructured or text-heavy data.
• Working knowledge of forecasting, regression, and basic statistical modeling (e.g., ARIMA, Prophet, linear regression, or similar methods).
• Familiarity with text classification / NLP workflows (e.g., scikit-learn, spaCy, Hugging Face).
• Awareness of AI-assisted development tools and basic understanding of generative AI / LLM concepts and their applications in data enrichment and automation.
• Strong understanding of data hygiene, classification logic, and dimensional modeling.
• Comfortable working in data warehouses (Snowflake, Redshift, etc.).
• Ability to create repeatable, documented transformation logic.

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