Data Product Analyst

CanadaRemoteFull-timemid

Data AnalystData

1 views0 saves0 applied

Apply Now

Quick Summary

Overview

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Product Analyst based in Canada.

Technical Tools

Data AnalystData

The Data Product Analyst will serve as a key link between business stakeholders and data engineering teams, ensuring that international data is accurate, reliable, and ready to support strategic decision-making. This role combines advanced SQL expertise, analytical thinking, and business analysis to manage data requirements from initial discovery through development, release, and ongoing monitoring. You will investigate data discrepancies, translate complex findings into actionable insights, and help stakeholders build confidence in the data they use. Working cross-functionally with analysts, architects, engineers, and business teams, you will drive data quality, clarify requirements, and ensure solutions meet real-world needs. You will also establish robust validation processes, strengthen monitoring capabilities, and proactively resolve issues throughout the data lifecycle. This is an opportunity to take ownership of critical data products in an international, collaborative, and technically driven environment.

Act as the subject matter expert for international data, developing an in-depth understanding of the end-to-end data supply chain through hands-on involvement in incoming requests and production incidents.
Build trust in data by partnering with business and marketing stakeholders to investigate discrepancies, resolve issues, and improve confidence in data accuracy and reliability.
Query, trace, and validate information from multiple sources to investigate anomalies, respond to ad hoc requests, identify trends, and uncover actionable insights.
Translate analytical findings into clear, business-friendly explanations that support decision-making and anticipate stakeholders’ follow-up questions.
Elicit, document, and manage business, functional, and non-functional requirements through stakeholder interviews, workshops, process analysis, and user story development.
Create detailed intake documentation that captures business needs, technical expectations, dependencies, and relevant edge cases.
Own delivery throughout the development lifecycle by coordinating with data engineering, tracking progress, validating completed work against requirements, and proactively driving issues to resolution.
Support prioritization decisions by evaluating business value, operational impact, technical complexity, and delivery risks.
Maintain requirements traceability from initial intake through development, testing, deployment, and production monitoring.
Collaborate with data analysts, architects, and engineers to ensure data solutions accurately reflect business context, intended use cases, and stakeholder expectations.
Define comprehensive acceptance criteria and user acceptance testing (UAT) scenarios, including data accuracy, completeness, integrity, and edge cases.
Support user validation, provide structured feedback, and identify opportunities to improve data products and delivery processes.
Monitor and improve data quality through profiling, validation, remediation, and alignment with business priorities.
Build and maintain business-rule monitoring mechanisms and alerts to detect logic-related issues that may not be identified by standard engineering checks.
Review historical incidents and monitoring gaps to strengthen controls and reduce the likelihood of recurring data issues.
Participate in production support group (PSG) discussions and release go/no-go meetings to understand upcoming changes that may affect data quality or reporting.
Manage time and availability effectively by prioritizing meeting participation, reviewing recordings when appropriate, and communicating scheduling constraints or workload concerns proactively.

Requirements

~2 min read

Bachelor’s degree in Information Systems, Computer Science, Data Analytics, or a related discipline, or equivalent practical experience.
At least five years of experience in data analysis, product analytics, analytics engineering, or a similar role, with extensive hands-on SQL experience.
Advanced SQL skills and strong analytical capabilities, with proficiency in Python for exploring, transforming, and validating complex datasets across multiple business and product domains.
Ability to independently investigate data issues, trace data lineage, verify data integrity, and validate findings without relying entirely on engineering teams.
Solid understanding of data modeling, ETL/ELT pipelines, and cloud-based data platforms, such as AWS, with the technical depth to collaborate effectively with data engineers.
Proven experience supporting cross-functional teams and delivering data products that meet the needs of multiple stakeholder groups.
Demonstrated ownership and accountability, including the ability to follow requests through development, release, and production while coordinating resolution across business and technical teams.
Strong requirements-gathering skills and the ability to translate business needs into clear, actionable technical specifications.
Experience writing user stories, defining acceptance criteria, and maintaining high-quality product and data documentation that supports consistent, scalable workflows.
Understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use.
Ability to connect and enrich datasets across multiple systems to generate deeper insights and support business objectives.
Excellent written and verbal communication skills, with the ability to adapt explanations for technical audiences, including engineering and data science teams, as well as non-technical stakeholders.
Strong interpersonal and collaboration skills, including the ability to influence without direct authority and build alignment across multidisciplinary teams.
Comfort working in ambiguous or evolving environments by asking clarifying questions, investigating unfamiliar data sources, and identifying opportunities for improvement.
Strong critical thinking, structured problem-solving, and statistical analysis skills, particularly when working with incomplete, inconsistent, or complex datasets.
Adaptability, attention to consistency and detail, results orientation, and effective time management, including the ability to work under pressure.
Familiarity with data visualization concepts and tools, including D3, is beneficial.