Data Analyst

Key Responsibilities

1. Data Product Ownership & Quality Assurance

• Own analytics-ready datasets and dashboards from requirements through delivery and adoption
• Lead QA and UAT processes for new datasets, pipelines, and data products
• Define acceptance criteria, test plans, and validation checks
• Ensure data accuracy, completeness, usability, and consistency
• Partner with data engineering to:
• Investigate root causes of data issues and bugs
• Prioritize fixes and enhancements
• Validate deployments before production release
• Act as a subject-matter expert for data definitions, metrics, and business logic
• Maintain documentation for data products, schemas, and testing standards

2. Analytics Enablement & Insight Delivery

• Query and validate complex datasets using SQL and Python
• Analyze large datasets to identify trends, patterns, and actionable insights
• Translate analytical findings into clear, business-friendly narratives
• Support end-to-end analytical deliverables from exploration through operationalization
• Ensure analytics outputs are trusted, repeatable, and fit for business use

3. Marketing & Experimentation Support

• Partner with marketing and business teams to:
• Enable audience segmentation for acquisition, engagement, and retention initiatives
• Define data needs for targeted and 1:1 campaign execution
• Support experimentation frameworks by:
• Defining test/control logic and success metrics
• Validating campaign data inputs and outputs
• Ensure campaign data flows are properly tested and production-ready

4. Stakeholder & Cross-Functional Collaboration

• Act as the primary liaison between business stakeholders and technical teams
• Elicit, document, and prioritize business requirements for analytics and data products
• Align privacy, legal, and compliance considerations into data design and testing
• Collaborate with internal teams and external partners to assess data quality and resolve discrepancies
• Promote shared understanding of data products and encourage best practices across teams

Skills & Competencies

Technical Skills

• Strong proficiency in SQL and working knowledge of Python
• Experience validating and testing data pipelines, datasets, and dashboards
• Understanding of data modeling concepts and analytical data structures
• Familiarity with cloud data platforms and modern data pipelines is a plus
• Experience working with consumer or customer data preferred

Product & QA Skills

• Experience defining business requirements, acceptance criteria, and success metrics
• Strong background in QA, UAT, and data validation workflows
• Ability to balance speed with data quality and governance
• Comfort operating as a product owner without direct engineering management

Soft Skills

• Excellent communication and data storytelling skills
• Strong critical thinking and problem-solving capabilities
• Ability to navigate ambiguity and drive clarity across teams
• Proven ability to influence stakeholders without formal authority
• High attention to detail and a proactive ownership mindset

Responsibilities

Location

• Full-time remote role
• Hybrid work option available for select locations (e.g., US or Canada(Toronto)

Qualifications

Required Qualifications

• Bachelor's degree in Analytics, Economics, Business Analysis, Marketing, or a related field
• 3+ years experience working with SQL, Python, or similar analytical tools
• 1+ year experience in stakeholder-facing roles or product ownership responsibilities
• Demonstrated experience supporting data-driven decision-making