Data Analyst (18-month Contract)

Flipp

3d•$76,000 - $80,000•Remote

About The Position

Flipp partners with the largest North American retailers and brands to deliver local promotions and savings to millions of engaged shoppers daily, driving superior returns on investments. We help people make smarter shopping decisions with autonomy and accountability. With rising living costs, Flipp's mission is crucial. Our Shopper Consideration Platform allows retailers and manufacturers to create digital experiences from their savings & deals content, aiding shoppers in deciding what to buy and where to buy it. Together, we make a difference. Our five principles, Progress Over Perfection, Clarity Through Transparency, Learn Loudly, Challenge with Empathy, and Always Build Better, bring a relentless progress mindset to life. They’re not just slogans, but they’re the behaviours we expect, reward, and hold ourselves accountable to. You'll be equipped to make an impact, realize your potential, and stay inspired every step of the way. Our Data Analytics team uncovers insights that shape product strategy, guide experimentation, and help the business understand what’s working and what comes next. As a trusted partner to Product, Engineering, and Business teams, the group turns complex data into clear stories that drive smarter decisions. We’re looking for a Data Analyst to join our Product Analytics team on an 18-month contract and support one of Flipp’s newest product initiatives: Digital Visual Merchandising (DVM), an initiative focused on enhancing the digital flyer experience while enabling more flexible, optimized, and targeted advertising content. If you enjoy solving complex problems, working closely with product teams, and turning data into meaningful insights that influence real product decisions, this role is for you.

Requirements

1–2 years of experience in a Data Analyst or Business Intelligence Analyst role
Strong experience with SQL/ PySpark/ Python and working with databases
Experience with BI and visualization tools such as Tableau, Sisense, Domo, or Amplitude
Experience analyzing large datasets and translating them into meaningful insights
Understanding of statistics and hypothesis testing (e.g., A/B testing)
Strong analytical thinking and problem-solving skills
Ability to translate business questions into analytical approaches
Excellent communication skills with the ability to explain data insights clearly to non-technical stakeholders

Nice To Haves

Experience working with product, digital, or marketplace data is a strong asset
More advanced statistical/ML experience for developing predictive models and feature (A/B) testing
Understanding of product analytics and digital marketing analytics
Understanding of retail/CPG industry or experience working in this field

Responsibilities

Partner with Product Teams to Drive Data-Informed Decisions
Work closely with Product Managers, Engineers, and cross-functional teams to understand business problems and translate them into analytical approaches.
Develop measurement frameworks to evaluate product features and experiments.
Analyze data to demonstrate product performance, uncover opportunities, and support product decision-making.
Break down complex business questions and deliver clear insights with limited guidance.
Build Reporting & Data Visualization
Design and develop dashboards that provide clear, actionable insights for stakeholders.
Apply best-practice visualization techniques to ensure dashboards are intuitive, performant, and scalable.
Partner with Data Engineers to develop and maintain semantic layers and data models that support self-serve analytics.
Enable Self-Serve Analytics & Data Adoption
Help teams access and interpret data through well-designed dashboards and reporting tools.
Monitor dashboard usage and identify opportunities to improve adoption and usability.
Train internal stakeholders on analytics tools, dashboards, and best practices.
Create documentation and internal resources that improve data literacy across teams.
Ensure Data Integrity & Quality
Work with data engineering teams to ensure reliable data pipelines and reporting infrastructure.
Identify data gaps or inconsistencies and help develop solutions.
Contribute to documentation such as data dictionaries and data standards.