Senior Data Product Analyst

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’re looking for a Senior Data Product Analyst who thrives at the intersection of data, analytics, and product development. You’ll play a central role in shaping Guideline’s data products — helping to transform large, complex datasets into actionable insights, power new features, and ensure the reliability of our data across production and releases.

This role combines analytical depth with product collaboration: working with engineers, product managers, and data strategists to evaluate datasets, automate QA workflows, and bring new capabilities to market.

The ideal candidate combines strong analytical skills with a product-first mindset—someone who can turn data into strategy, shape product direction, and ensure the integrity of the data powering every release.

Key Responsibilities

• Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production.
• Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality — all to drive successful adoption and launch readiness
• Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams.
• Design and implement automated data validation and quality frameworks for digital products — defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases.
• Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence.
• Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams.
• Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals.
• Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation.
• Partner with engineering teams to integrate machine-learning–based anomaly detection and data quality checks into the product pipeline.
• Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights.
• Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance

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:
- Health, dental, life, and disability insurance
- RRSP with company match
- Paid time off and parental leave
- Teledoc Health services
- Employee recognition and referral bonuses

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

• Extensive experience as a Data Analyst in a data-centric or product-driven environment, with the ability to translate analytical insights into product strategy and measurable business impact.
• Strong analytical and problem-solving skills — able to identify trends, anomalies, and opportunities in large datasets.
• Proficiency in SQL for querying, transforming, and validating complex datasets.
• Hands-on experience with Power BI or equivalent BI tools (Tableau, Looker, Sisense, Qlik).
• Demonstrated ability to communicate complex findings clearly, collaborate effectively with cross-functional teams, and influence decisions across Product, Engineering, and Operations.
• Highly organized, detail-oriented, and proactive in managing multiple priorities and deadlines.

Preferred Skills & Experience

• Background in media, advertising, or marketing data, with an understanding of spend, pricing, or campaign datasets.
• Familiarity with MediaOcean (Prisma, Spectra), HudsonMX, or WideOrbit platforms.
• Experience implementing automated QA systems, anomaly detection, or alerting frameworks for data quality.
• Knowledge of JIRA, Confluence, and agile product development practices.
• Strong data storytelling and business acumen, able to synthesize insights and present recommendations to senior stakeholders.
• Familiarity with AI-assisted analytics tools (e.g., ChatGPT, Copilot, AI-based BI platforms).
• Understanding of how data pipelines support AI models, including data labeling, data quality, and governance considerations.
• Exposure to LLM workflows, embeddings, or vector-based search concepts (nice to have, not required).
• Experience with Python-based data analysis or lightweight ML experimentation (scikit-learn preferred but not required).