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Data Product Analyst (International Data)

Wise Skulls Corp.

Toronto, Ontario • 2d ago

Easy Apply

Full-time

Depends on Experience

Staff Product Data Analyst, Fraud & Surveillance

NinjaTrader

Remote or Chicago, Illinois • Today

Full-time

USD 162,000.00 - 212,000.00 per year

Data Product Analyst, Corporate

YipitData

Remote • Today

Full-time

Job Details

Skills

Amazon Web Services
Acceptance Testing
Business Rules
Computer Science
Conflict Resolution
Continuous Improvement
Critical Thinking
Cloud Computing
Development Testing
Extract, Transform, Load
Problem Solving
Requirements Elicitation
Specification Gathering
Software Release Life Cycle
Data Stewardship
Data Engineering

Summary

Overview

The Data Product Analyst serves as a critical bridge between business stakeholders and data engineering for the international data platform. This role combines strong SQL and technical depth with core business analysis skills to ensure that data is trustworthy, accurately specified, and effectively leveraged to inform decision making. The Data Product Analyst owns the path from business request to trusted data in production: eliciting complete requirements, following the work through build and release, and monitoring the data once it is live. Over time, the role becomes the subject matter expert for international data.

Key Responsibilities

Act as the Data Subject Matter Expert to stakeholders, building deep knowledge of international data across the endtoend data supply chain through hands-on work on every intake and production incident
Make international data trustworthy by working directly with business and marketing stakeholders to resolve data issues and build their confidence in the data.
Query, trace, and validate data from multiple sources to investigate anomalies, answer ad hoc requests, and uncover trends, patterns, and actionable insights.
Translate analytical findings into clear, businessfriendly insights that support decisionmaking.
Go beyond producing numbers: state what they mean in a sentence and anticipate the stakeholder's next question.
Elicit, document, and manage business, functional, and nonfunctional requirements through techniques such as interviews, workshops, process analysis, and user story creation. Write complete intakes that capture every requirement in as much detail as possible.
Own delivery after handoff: check in regularly with data engineering, confirm that what is built matches what was requested, and drive issues to resolution without waiting for direction.
Support prioritization by assessing business value, impacts, complexity, and risk.
Ensure traceability of requirements from intake through development, testing, and deployment.
Collaborate with data engineering analysts, architects, and engineers to ensure solutions reflect business context and use cases.
Define acceptance and UAT criteria that go beyond what the business specifies, including data accuracy, completeness, and edge cases; support user validation and provide structured feedback for continuous improvement.
Monitor and enhance data quality through profiling, validation, remediation, and alignment with organizational priorities.
Build and maintain business-rule monitors and alerts that catch business logic issues engineering's technical checks may miss, and review past incidents to close monitoring gaps.
Participate in PSG and release go/no-go meetings to stay informed on production changes that may affect data quality or reporting.
Manage time and availability: decide which meetings need active participation versus a recording, and raise calendar overload or availability issues early.

Key Skills & Competencies:

Strong SQL proficiency, with the ability to independently explore tables, trace lineage, and validate data without relying on engineering.
Skilled at requirements elicitation and translating business needs into technical specifications.
Skilled in crafting user stories, acceptance criteria, and highquality product and data documentation to support scalable, repeatable workflows.
Strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use and the ability to adapt analytical and stewardship skills across a wide range of use cases.
Comfortable connecting data across multiple systems and enriching datasets to enable new insights for business stakeholders.
Excellent communication, with the ability to tailor messages for both technical teams (engineering, data science) and business stakeholders.
Collaborative mindset with the ability to influence without authority and drive alignment across cross functional teams.
Skilled at navigating ambiguity, asking the right clarifying questions, and moving teams toward clarity and action.
Naturally curious, with a passion for exploring new data sources, uncovering insights, and improving data quality.
Strong critical thinking and structured problemsolving, especially when interpreting incomplete, messy, or evolving datasets.
Strong analytical toolkit leveraging SQL and other data querying tools (Python is a plus) to explore, transform, and validate complex datasets that span across multiple product disciplines.
Solid technical background in data modeling, ETL/ELT pipelines, and cloud data platforms (e.g., AWS), with enough depth to understand how data products are built and to partner credibly with engineers.

Behavioral Competencies

Ownership and accountability: follows work through to the outcome without being asked and stays engaged after handoff.
Proactivity: takes initiative ("shall I go ahead and do this?") and keeps work moving even when no deadline is set.
Speaks up: raises questions, concerns, and risks openly in meetings rather than only in side conversations.
Transparency: communicates availability, blockers, and workload early.

Qualifications

Bachelor's degree in information systems, computer science, data analytics, or a related field, or equivalent experience.
5+ years of experience in data analysis, product analytics, analytics engineering, or similar roles, with extensive hands-on SQL experience.
Proven ability to work effectively in cross functional environments and support the delivery of data products that serve multiple stakeholders.
Demonstrated record of ownership: following requests through delivery and production and resolving issues across business and engineering teams.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91097138
Position Id: 9127614
Posted 2 days ago

Fitment

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Contact the job poster

Vijay Tade

Recruiter @ Wise Skulls Corp.

View Profile

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Job Details

Skills

Amazon Web Services
Acceptance Testing
Business Rules
Computer Science
Conflict Resolution
Continuous Improvement
Critical Thinking
Cloud Computing
Development Testing
Extract, Transform, Load
Problem Solving
Requirements Elicitation
Specification Gathering
Software Release Life Cycle
Data Stewardship
Data Engineering

Summary

Overview

The Data Product Analyst serves as a critical bridge between business stakeholders and data engineering for the international data platform. This role combines strong SQL and technical depth with core business analysis skills to ensure that data is trustworthy, accurately specified, and effectively leveraged to inform decision making. The Data Product Analyst owns the path from business request to trusted data in production: eliciting complete requirements, following the work through build and release, and monitoring the data once it is live. Over time, the role becomes the subject matter expert for international data.

Key Responsibilities

Act as the Data Subject Matter Expert to stakeholders, building deep knowledge of international data across the endtoend data supply chain through hands-on work on every intake and production incident
Make international data trustworthy by working directly with business and marketing stakeholders to resolve data issues and build their confidence in the data.
Query, trace, and validate data from multiple sources to investigate anomalies, answer ad hoc requests, and uncover trends, patterns, and actionable insights.
Translate analytical findings into clear, businessfriendly insights that support decisionmaking.
Go beyond producing numbers: state what they mean in a sentence and anticipate the stakeholder's next question.
Elicit, document, and manage business, functional, and nonfunctional requirements through techniques such as interviews, workshops, process analysis, and user story creation. Write complete intakes that capture every requirement in as much detail as possible.
Own delivery after handoff: check in regularly with data engineering, confirm that what is built matches what was requested, and drive issues to resolution without waiting for direction.
Support prioritization by assessing business value, impacts, complexity, and risk.
Ensure traceability of requirements from intake through development, testing, and deployment.
Collaborate with data engineering analysts, architects, and engineers to ensure solutions reflect business context and use cases.
Define acceptance and UAT criteria that go beyond what the business specifies, including data accuracy, completeness, and edge cases; support user validation and provide structured feedback for continuous improvement.
Monitor and enhance data quality through profiling, validation, remediation, and alignment with organizational priorities.
Build and maintain business-rule monitors and alerts that catch business logic issues engineering's technical checks may miss, and review past incidents to close monitoring gaps.
Participate in PSG and release go/no-go meetings to stay informed on production changes that may affect data quality or reporting.
Manage time and availability: decide which meetings need active participation versus a recording, and raise calendar overload or availability issues early.

Key Skills & Competencies:

Strong SQL proficiency, with the ability to independently explore tables, trace lineage, and validate data without relying on engineering.
Skilled at requirements elicitation and translating business needs into technical specifications.
Skilled in crafting user stories, acceptance criteria, and highquality product and data documentation to support scalable, repeatable workflows.
Strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use and the ability to adapt analytical and stewardship skills across a wide range of use cases.
Comfortable connecting data across multiple systems and enriching datasets to enable new insights for business stakeholders.
Excellent communication, with the ability to tailor messages for both technical teams (engineering, data science) and business stakeholders.
Collaborative mindset with the ability to influence without authority and drive alignment across cross functional teams.
Skilled at navigating ambiguity, asking the right clarifying questions, and moving teams toward clarity and action.
Naturally curious, with a passion for exploring new data sources, uncovering insights, and improving data quality.
Strong critical thinking and structured problemsolving, especially when interpreting incomplete, messy, or evolving datasets.
Strong analytical toolkit leveraging SQL and other data querying tools (Python is a plus) to explore, transform, and validate complex datasets that span across multiple product disciplines.
Solid technical background in data modeling, ETL/ELT pipelines, and cloud data platforms (e.g., AWS), with enough depth to understand how data products are built and to partner credibly with engineers.

Behavioral Competencies

Ownership and accountability: follows work through to the outcome without being asked and stays engaged after handoff.
Proactivity: takes initiative ("shall I go ahead and do this?") and keeps work moving even when no deadline is set.
Speaks up: raises questions, concerns, and risks openly in meetings rather than only in side conversations.
Transparency: communicates availability, blockers, and workload early.

Qualifications

Bachelor's degree in information systems, computer science, data analytics, or a related field, or equivalent experience.
5+ years of experience in data analysis, product analytics, analytics engineering, or similar roles, with extensive hands-on SQL experience.
Proven ability to work effectively in cross functional environments and support the delivery of data products that serve multiple stakeholders.
Demonstrated record of ownership: following requests through delivery and production and resolving issues across business and engineering teams.

Dice Id: 91097138
Position Id: 9127614
Posted 2 days ago

Fitment