Remote Product Strategy Manager

📍 Job Overview

Job Title: Principal Data Product Manager Company: Jobgether (on behalf of a partner company) Location: Netherlands Job Type: Full-time Category: Data & Analytics / Product Management Date Posted: January 12, 2026 Experience Level: 10+ years Remote Status: Fully Remote

🚀 Role Summary

• Lead the strategic vision and execution of data products and analytics capabilities, directly influencing organizational competitiveness and strategic decision-making.
• Drive the definition and delivery of complex, high-impact data solutions, from discovery and validation through to end-to-end architectural design and implementation.
• Own and manage a multi-year roadmap for enterprise data products, ensuring alignment with overarching business objectives and growth strategies.
• Act as a pivotal influencer for key stakeholders, translating intricate data concepts into actionable insights and fostering cross-functional alignment on critical data initiatives.

📝 Enhancement Note: This role is a "Principal" level position, indicating a senior leadership capacity focused on strategic direction and significant impact. The emphasis on "data products" specifically targets professionals with experience in developing, managing, and scaling data as a core offering or enabler, rather than just traditional product management. The remote nature within the Netherlands suggests a need for strong self-management and communication skills, as well as familiarity with distributed team collaboration.

📈 Primary Responsibilities

• Partner with executive leadership to establish and champion a strategic vision for data products and advanced analytics capabilities across the organization.
• Lead comprehensive discovery and validation processes to identify and prioritize transformational data product opportunities that address critical business needs.
• Develop, own, and effectively communicate a multi-year roadmap for enterprise-level data products, ensuring clear alignment with strategic business objectives and market demands.
• Architect and define end-to-end data product solutions, integrating various data systems, platforms, and technologies to ensure scalability, reliability, and performance.
• Conduct in-depth data analysis using advanced SQL queries to uncover key trends, identify actionable insights, and quantify the business impact of data initiatives.
• Collaborate closely with data science and engineering teams to guide machine learning model development, analytical projects, and the operationalization of data-driven features.
• Translate complex data concepts, methodologies, and findings into clear, concise, and compelling language for non-technical stakeholders, including executive leadership.
• Facilitate and drive cross-functional alignment and buy-in on complex data initiatives, ensuring seamless collaboration between product, engineering, data science, and business units.
• Define, measure, and monitor key success metrics and comprehensive KPI frameworks for all data products, ensuring performance against strategic goals.
• Mentor and guide junior product managers and data professionals, fostering a culture of best practice adoption and continuous improvement within the data product function.
• Research, evaluate, and recommend new data technologies, platforms, and methodologies to enhance the organization's data capabilities and competitive edge.
• Drive organizational change management initiatives related to data product adoption and data-driven decision-making processes.
• Represent the Data Product team in executive forums, strategic planning sessions, and key stakeholder meetings, advocating for data-centric strategies.

📝 Enhancement Note: The responsibilities highlight a blend of strategic leadership, technical depth, and cross-functional influence. The emphasis on "partnering with executives," "transformational opportunities," and "multi-year roadmap" points to a senior role with significant autonomy and impact on the company's data strategy. The inclusion of "data analysis using SQL," "data modeling," "data architecture," and "data science methodologies" clearly defines the technical domain expertise required.

🎓 Skills & Qualifications

Education: While no specific degree is mandated, a strong academic background in a quantitative field such as Computer Science, Data Science, Engineering, Statistics, Mathematics, Business Analytics, or a related discipline is highly valued. Equivalent practical experience will also be considered.

Experience:

• A minimum of 10+ years of progressive experience in product management, data analytics, or a closely related field.
• At least 5 years of dedicated experience focusing specifically on data products, advanced analytics platforms, or data-as-a-service initiatives.
• Demonstrated success in leading and delivering complex, cross-functional data initiatives from conception to successful deployment and adoption.

Required Skills:

• Product Management: Proven ability to define product vision, strategy, and roadmap, with a strong emphasis on data-centric products.
• Data Analytics & SQL: Expert-level proficiency in SQL for complex data querying, manipulation, and analysis; ability to derive actionable insights from large datasets.
• Data Product Development: Deep understanding of the data product lifecycle, including discovery, validation, design, development, deployment, and iteration.
• Data Architecture & Modeling: Strong knowledge of data modeling principles, data warehousing concepts, and modern data architecture patterns (e.g., data lakes, lakehouses).
• Strategic Thinking: Ability to develop and articulate a compelling long-term vision for data products, aligning with business strategy and identifying ROI opportunities.
• Communication & Presentation: Exceptional verbal and written communication skills, with a proven ability to present complex technical information clearly and persuasively to executive audiences.
• Project Management: Robust project management capabilities, adept at managing complex, multi-stakeholder initiatives, timelines, and dependencies.
• Cross-Functional Collaboration: Demonstrated ability to effectively collaborate with and influence diverse teams including data scientists, engineers, designers, and business stakeholders.

Preferred Skills:

• Data Science & ML: Working knowledge of data science methodologies, machine learning concepts, and experience collaborating with ML engineers on model deployment and integration.
• Modern Data Stacks: Familiarity with contemporary data stacks, big data technologies (e.g., Spark, Hadoop), and cloud-based data platforms (AWS, Azure, GCP).
• Mentorship: Experience in mentoring junior product managers or analysts, fostering skill development and best practice adoption.
• Change Management: Proven ability to drive organizational change and promote data literacy and data-driven decision-making.
• Agile Methodologies: Experience working within Agile/Scrum frameworks for product development.
• Business Acumen: Strong understanding of business operations, market dynamics, and how data products can drive competitive advantage and business value.

📝 Enhancement Note: The "10+ years" and "5+ years focused on data products" clearly positions this as a senior, principal-level role. The deep dive into SQL, data architecture, and data science methodologies is critical for success. The "preferred skills" offer a roadmap for candidates to highlight additional strengths that would make them stand out.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Data Product Case Studies: Showcase 2-3 detailed case studies of data products you have conceptualized, developed, and launched. Each case study should clearly articulate the problem statement, your strategic approach, the data and technology stack used, key challenges overcome, metrics of success, and the quantifiable business impact (e.g., revenue increase, cost reduction, efficiency gains).
• Roadmap Development Examples: Provide examples demonstrating your ability to create and communicate multi-year strategic roadmaps for data products or analytics platforms. This could include visual representations, strategic documents, or presentations outlining prioritization frameworks and phased development plans.
• SQL Analysis Demonstrations: Include examples of complex SQL queries you have written to perform in-depth data analysis, identify trends, and support strategic decision-making. Highlight the insights derived and how they translated into business actions.
• Stakeholder Communication Artifacts: Present examples of how you have translated complex data concepts for non-technical audiences, such as executive summaries, dashboard designs, or presentation slides used to gain buy-in for data initiatives.

Process Documentation:

• Product Discovery & Validation: Document your systematic approach to identifying and validating new data product opportunities, including methodologies for market research, user needs assessment, and feasibility studies.
• Data Product Development Lifecycle: Outline your process for managing the end-to-end lifecycle of a data product, from ideation and requirements gathering through to development, testing, deployment, and ongoing iteration.
• Metrics & KPI Frameworks: Demonstrate experience in designing and implementing robust metrics and KPI frameworks to measure the performance, adoption, and business value of data products.
• Cross-Functional Collaboration Workflows: Illustrate your established workflows for collaborating effectively with data science, engineering, and business teams to ensure alignment, facilitate communication, and drive successful project outcomes.

📝 Enhancement Note: For a Principal Data Product Manager role, a portfolio is crucial. It needs to demonstrate not just management skills, but strategic depth, technical proficiency in data, and the ability to translate data into tangible business value. The emphasis on ROI, complex SQL, and roadmap articulation is key.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: €120,000 - €170,000 per year (gross)
• Explanation: This estimate is based on industry benchmarks for Principal-level Product Management roles with a strong data focus, particularly in the Netherlands. Factors considered include the extensive experience requirement (10+ years), the strategic nature of the role, and the fully remote, international nature of the position. The range accounts for variations in candidate experience, specific skill sets, and the partner company's compensation structure. Research was conducted using data from reputable compensation platforms and industry reports for senior tech and product roles in Western Europe.

Benefits:

• Competitive Salary & Equity Options: A strong base salary complemented by equity options, aligning your success with the company's growth.
• 401K with Company Match: Financial planning support through a retirement savings plan with employer contributions. (Note: While 401K is US-centric, this may indicate a US-based parent company or a global benefits package that includes equivalent retirement savings plans in the Netherlands, such as a "pensioenregeling").
• Flexible PTO and Holidays: Generous paid time off policy, allowing for work-life balance and personal well-being.
• Paid Parental Leave Program: Comprehensive support for new parents, including salary continuation during leave.
• Education and Professional Development Opportunities: Investment in your continuous learning through training, certifications, conferences, and other development initiatives.
• Comprehensive Health Benefits: Robust health coverage, including medical, dental, and vision insurance, ensuring your well-being.

Working Hours:

• Standard full-time commitment, typically around 40 hours per week.
• Given the fully remote nature and international team, flexibility in working hours is expected to accommodate collaboration across different time zones, while ensuring core availability for key meetings and team syncs.

📝 Enhancement Note: The mention of "401K" is unusual for a Netherlands-based role and might indicate a US-headquartered company with global benefits. It's advisable to clarify the specific retirement savings plan structure. The salary range is an estimation for a senior role in a high-cost-of-living European country.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / Software / Data Solutions (as inferred from the role and Jobgether's focus) Company Size: The "Principal" level and complex data initiatives suggest a mid-to-large sized organization, likely a scale-up or established tech company that is investing heavily in its data capabilities. This implies a dynamic environment with significant resources and potential for impact. Founded: The exact founding date of the partner company is not specified, but the focus on advanced data products and strategic vision suggests a company that is either mature enough to invest in such initiatives or a rapidly growing entity prioritizing data as a core differentiator.

Team Structure:

• Data Product Team: Likely a dedicated team of data product managers, potentially with specialized roles overseeing different aspects of the data product portfolio.
• Reporting Structure: The Principal Data Product Manager will likely report to a senior executive, such as a VP of Product, Chief Data Officer (CDO), or CTO, given the strategic scope and executive-level collaboration required.
• Cross-functional Collaboration: This role is inherently cross-functional, requiring close partnerships with Data Science, Data Engineering, Software Engineering, Business Intelligence, Marketing, Sales, and Executive Leadership teams.

Methodology:

• Data-Driven Decision-Making: A core tenet, where strategies and product decisions are heavily influenced by data analysis and insights.
• Agile Product Development: Likely employing Agile methodologies for iterative development, continuous feedback, and rapid adaptation.
• Strategic Planning: Emphasis on long-term vision setting, roadmap development, and resource allocation based on strategic priorities and ROI potential.
• Innovation & Experimentation: Encouragement to explore new technologies and data applications to drive innovation and maintain a competitive edge.

Company Website: https://jobgether.com/ (Note: This is Jobgether's website, the partner company's website is not directly provided.)

📝 Enhancement Note: The description focuses on the partner company's needs, not Jobgether itself. The role's strategic depth and focus on data products suggest a company that views data as a critical asset and competitive advantage.

📈 Career & Growth Analysis

Operations Career Level: Principal Data Product Manager. This is a senior leadership position, often considered one to two levels below VP. It signifies deep expertise, strategic influence, and the ability to lead major initiatives independently. This role is for experienced professionals who are ready to define direction and mentor others.

Reporting Structure: The Principal Data Product Manager is expected to report to a senior executive (e.g., VP of Product, Chief Data Officer, CTO). This provides high visibility and direct access to executive decision-making processes, allowing for significant influence on company strategy.

Operations Impact: The impact is substantial, focusing on shaping the "data product portfolio" and driving "competitiveness" through "transformational data product opportunities." This role directly influences how the organization leverages data for strategic advantage, revenue generation, operational efficiency, and innovation. Success is measured by the tangible business outcomes derived from the data products managed.

Growth Opportunities:

• Leadership Advancement: Potential to move into Director or VP-level roles in Product Management, Data Strategy, or Analytics, overseeing larger teams and broader portfolios.
• Specialization: Opportunity to deepen expertise in specific areas of data science, AI/ML productization, or advanced analytics platforms.
• Strategic Influence: Continued growth in shaping company-wide data strategy and influencing executive-level decisions.
• Mentorship & Team Building: Opportunity to build and lead teams, establishing data product management as a core competency within the organization.

📝 Enhancement Note: The "Principal" title inherently implies significant growth potential and a high level of impact. The focus is on strategic leadership and shaping the future of data within the organization.

🌐 Work Environment

Office Type: Fully Remote. This indicates a distributed workforce with no central office requirement. The company likely has established remote work policies and infrastructure to support this model.

Office Location(s): While the role is remote, the mention of "Netherlands" as the location implies that candidates are expected to be based within the Netherlands, or potentially within a European time zone that aligns closely with Dutch working hours. This could be due to tax, legal, or operational reasons.

Workspace Context:

• Autonomy & Self-Management: A high degree of autonomy is expected, requiring strong self-discipline, time management, and proactive communication.
• Digital Collaboration Tools: Reliance on a robust suite of digital collaboration tools (e.g., Slack, Zoom, Microsoft Teams, Asana, Jira) for communication, project management, and documentation.
• Virtual Team Engagement: Opportunities for virtual team-building activities, knowledge-sharing sessions, and collaborative problem-solving through online platforms.

Work Schedule:

• Full-time, approximately 40 hours per week.
• Flexibility is a key aspect of remote work, allowing individuals to structure their day around core collaboration hours and personal needs. However, availability during standard Dutch business hours (CET) is likely expected for team meetings and critical interactions.

📝 Enhancement Note: The "Netherlands" location for a remote role suggests a specific geographical focus for hiring, possibly for legal or tax compliance. The work environment emphasizes self-direction and proficiency with remote collaboration tools.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening (Jobgether): An AI-powered matching process by Jobgether to assess core requirements, followed by a review by their team.
• Hiring Manager Interview: A discussion focused on strategic thinking, product vision, data product experience, and alignment with the role's core accountabilities. Be prepared to discuss your approach to defining data product roadmaps and strategy.
• Technical/Domain Interview: In-depth assessment of your SQL proficiency, data modeling knowledge, understanding of data architecture, and experience with data science methodologies. Expect scenario-based questions and potentially a live coding or analysis exercise.
• Cross-Functional/Stakeholder Interview: Interaction with key stakeholders from engineering, data science, and business units to assess collaboration skills, communication effectiveness, and ability to influence.
• Executive Interview: A final conversation with senior leadership (e.g., CDO, VP Product) to evaluate strategic alignment, leadership potential, and overall fit for the Principal level.

Portfolio Review Tips:

• Quantify Impact: For each case study, clearly articulate the measurable business impact (ROI, revenue growth, cost savings, efficiency improvements) using concrete numbers and key metrics.
• Showcase Strategic Thinking: Demonstrate how your data product strategy aligned with broader business goals and how you made prioritization decisions.
• Detail Your Process: Explain your methodology for product discovery, roadmap planning, and stakeholder management. Use diagrams or process flows where helpful.
• Technical Depth: Be ready to discuss the technical challenges you faced and how you leveraged SQL, data architecture, and data science principles to overcome them.
• Conciseness: While detailed, ensure your portfolio is well-organized and easy to navigate. Highlight the most impactful projects prominently.

Challenge Preparation:

• Data Strategy Case Study: Be prepared for a hypothetical scenario where you need to define a data product strategy for a given business problem. Focus on identifying opportunities, defining metrics, proposing a roadmap, and outlining potential risks.
• SQL Problem Solving: Practice complex SQL queries involving joins, aggregations, window functions, and subqueries. Be ready to analyze a dataset and extract specific business insights.
• Stakeholder Alignment Scenario: Prepare to discuss how you would gain buy-in from different departments for a new data product initiative, addressing potential concerns and highlighting mutual benefits.

📝 Enhancement Note: The portfolio review is critical for this role. Candidates should be ready to present detailed case studies that demonstrate strategic thinking, technical acumen, and measurable business impact. The interview process is designed to assess these capabilities at a senior level.

🛠 Tools & Technology Stack

Primary Tools:

• SQL: Expert-level proficiency is a non-negotiable requirement for data extraction, manipulation, and analysis.
• Data Product Management Platforms: Experience with tools that support roadmap planning, backlog management, and product lifecycle tracking (e.g., Jira, Aha!, Productboard).
• Collaboration Suites: Proficiency in tools like Slack, Microsoft Teams, Zoom for day-to-day communication and virtual meetings.
• Documentation Tools: Experience with Confluence, Google Workspace, or similar for creating and sharing product documentation, specifications, and strategy documents.

Analytics & Reporting:

• Business Intelligence (BI) Tools: Familiarity with BI platforms such as Tableau, Power BI, Looker, or QlikView for data visualization and dashboard creation.
• Data Warehousing Technologies: Understanding of concepts and potentially experience with platforms like Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse Analytics.
• ETL/ELT Tools: Knowledge of tools or processes involved in data extraction, transformation, and loading (e.g., Fivetran, Stitch, Talend).

CRM & Automation:

• CRM Systems: While not primary, understanding how data products integrate with or support CRM functions (e.g., Salesforce, HubSpot) is beneficial for understanding business context.
• Cloud Platforms: Experience with major cloud providers (AWS, Azure, GCP) and their respective data services is highly advantageous.
• Data Science/ML Platforms: Familiarity with tools and environments used for machine learning model development and deployment (e.g., Databricks, Sagemaker, Vertex AI).

📝 Enhancement Note: The emphasis is on deep SQL expertise and familiarity with modern data stacks and cloud platforms. The role requires managing data products, so tools that support the product lifecycle and collaboration are also key.

👥 Team Culture & Values

Operations Values:

• Data-Driven Excellence: A commitment to leveraging data rigorously to inform strategy, measure impact, and drive continuous improvement in all aspects of data product development.
• Strategic Impact: A focus on initiatives that deliver significant, measurable business value and contribute directly to the company's competitive advantage and growth objectives.
• Collaboration & Transparency: Fostering an environment of open communication, knowledge sharing, and mutual respect across diverse teams to achieve shared goals.
• Innovation & Adaptability: Encouraging curiosity, experimentation with new technologies, and the ability to adapt quickly to evolving data landscapes and business needs.
• Customer Focus: Ensuring that data products are designed and delivered with the end-user (internal or external) needs at the forefront, driving adoption and satisfaction.

Collaboration Style:

• Proactive & Consultative: Engaging early and often with stakeholders to understand needs, align on strategy, and build consensus.
• Cross-Functional Partnership: Working as an integral part of cross-functional teams, acting as a bridge between technical data capabilities and business objectives.
• Mentorship & Knowledge Sharing: Actively sharing expertise, best practices, and insights with colleagues to elevate the team's collective capabilities.
• Feedback-Oriented: Creating and participating in a culture where constructive feedback is welcomed and used for continuous improvement of products and processes.

📝 Enhancement Note: The values emphasize a strong data-centric approach, strategic impact, and collaborative execution. This role requires someone who can champion data as a strategic asset and work effectively across different functional areas.

⚡ Challenges & Growth Opportunities

Challenges:

• Defining a Unified Data Strategy: Navigating complex organizational structures and diverse stakeholder needs to establish a cohesive and prioritized data product roadmap.
• Translating Technical Complexity: Effectively communicating the value and intricacies of advanced data products and AI/ML capabilities to non-technical executives and teams.
• Driving Adoption & Change Management: Overcoming resistance to new data products or data-driven processes, and ensuring widespread adoption and effective utilization.
• Keeping Pace with Technology: Continuously evaluating and integrating emerging data technologies and methodologies to maintain a competitive edge in a rapidly evolving field.
• Measuring ROI for Data Products: Establishing clear metrics and attribution models to demonstrate the tangible business value and return on investment derived from data initiatives.

Learning & Development Opportunities:

• Advanced Data Strategy: Deepening expertise in enterprise data strategy, data governance, and data ethics.
• AI/ML Productization: Gaining hands-on experience in bringing sophisticated AI and machine learning models to market as successful products.
• Leadership Development: Opportunities to hone executive communication, strategic planning, and team leadership skills.
• Industry Conferences & Certifications: Access to relevant industry events, training programs, and certifications to stay at the forefront of data product management and technology.
• Cross-Industry Exposure: Potentially working on diverse data product challenges that span different business units or market segments, broadening your experience.

📝 Enhancement Note: The challenges are typical for a senior role focused on data strategy and transformation. The growth opportunities are geared towards continued leadership development and specialization in cutting-edge data domains.

💡 Interview Preparation

Strategy Questions:

• "Describe your process for identifying and validating a new data product opportunity. How do you prioritize these opportunities against competing business needs?" (Focus on your discovery, validation, and prioritization frameworks, linking them to business objectives and ROI.)
• "How would you build a multi-year data product roadmap for an organization like ours? What are the key considerations and how do you ensure stakeholder alignment?" (Prepare to discuss roadmap structure, dependency management, and communication strategies for executive buy-in.)
• "Walk me through a complex data product you managed from concept to launch. What were the biggest technical and business challenges, and how did you overcome them? What was the measurable impact?" (This is where your portfolio case studies shine. Be detailed, data-driven, and articulate the ROI.)

Company & Culture Questions:

• "What do you know about our company's current data maturity and how do you see a Principal Data Product Manager contributing to our strategic goals?" (Research the partner company's industry, market position, and any publicly available information on their data initiatives.)
• "How do you approach building relationships and driving consensus with diverse technical and non-technical stakeholders?" (Provide examples of your collaboration style and conflict resolution skills.)
• "How do you measure the success of a data product beyond usage metrics? How do you quantify its business value?" (Focus on your approach to defining KPIs and linking them to tangible business outcomes.)

Portfolio Presentation Strategy:

• Structure for Impact: Organize your portfolio logically, prioritizing your most relevant and impactful data product case studies. Use a consistent template for each case study (Problem, Solution, Data/Tech, Challenges, Impact/Metrics).
• Storytelling with Data: Weave a narrative around each project, highlighting your strategic thinking, problem-solving approach, and the ultimate business value delivered. Use visuals (charts, dashboards, architecture diagrams) where appropriate.
• Quantify Everything: Emphasize the measurable results. Be prepared to defend your ROI calculations and the data used to derive them.
• Technical Fluency: Be ready to dive deep into the technical aspects of your projects, discussing your SQL expertise, data modeling choices, and architectural decisions.
• Concise Delivery: Practice presenting your key projects within a limited timeframe, focusing on the most critical aspects and outcomes.

📝 Enhancement Note: Preparation should focus on demonstrating strategic vision, deep technical understanding of data products, and a proven ability to drive measurable business impact. The portfolio is the centerpiece of the application.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on Lever.
• Tailor Your Resume: Ensure your resume highlights your 10+ years of experience, with a specific emphasis on the 5+ years dedicated to data products. Use keywords from the job description such as "Data Products," "SQL," "Data Strategy," "Roadmap," "Analytics," "Stakeholder Management," and "ROI." Quantify achievements wherever possible.
• Prepare Your Portfolio: Curate 2-3 strong data product case studies that showcase your strategic thinking, technical skills, and measurable business impact. Ensure it's well-organized and easy to navigate for review.
• Practice Your Pitch: Rehearse your ability to articulate your experience, particularly your approach to data product strategy, roadmap development, and stakeholder collaboration. Be ready to present key portfolio items concisely.
• Research the Partner Company: Invest time in understanding the partner company's industry, market position, and any available information on their data initiatives to tailor your responses and demonstrate genuine interest.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.