Data Analyst: Prospect UI Solutions (Remote)

📍 Job Overview

Job Title: Data Analyst: Prospect UI Solutions

Company: Constructor

Location: Argentina

Job Type: FULL_TIME

Category: Sales Operations / Revenue Operations Support

Date Posted: 2025-09-22T10:15:10.371

Experience Level: Mid-Level (2-5 years)

Remote Status: Fully Remote

🚀 Role Summary

• Support the sales and account executive team by developing critical analytical tools and dashboards to enhance customer demonstrations, directly impacting the sales pipeline and conversion rates.

• Collaborate closely with sales and engineering teams to fine-tune AI models, ensuring optimal performance and relevance for prospective clients, thereby accelerating deal closure.

• Drive product innovation by conducting in-depth user behavior analysis and data exploration to identify opportunities for enhancing recommendation and search systems, contributing to the company's competitive edge.

• Foster a strong team culture by actively participating in and promoting community-building activities, emphasizing empathy, personal growth, and mutual support.

📝 Enhancement Note: While the title is "Data Analyst," the core responsibilities and collaboration with sales teams (Account Executives) strongly indicate a role within Sales Operations or Revenue Operations support, focusing on enabling the go-to-market (GTM) function through data insights and tool development. The emphasis on "prospect UI solutions" and "tuning AI models" for sales demos points to a direct impact on the sales funnel.

📈 Primary Responsibilities

• Design, develop, and maintain analytical tools and interactive dashboards for Account Executives to improve the quality and effectiveness of sales demonstrations for prospective clients.

• Collaborate with Account Executives and Frontend Engineers to fine-tune AI models, ensuring they are optimized for specific prospect needs and showcase Constructor's value proposition accurately.

• Conduct deep-dive exploratory data analysis using Python (pandas, NumPy) and SQL to understand user behavior patterns on the platform and identify actionable insights for product improvement.

• Translate complex business requirements from sales and product teams into technical specifications for data analysis and tool development, ensuring alignment with GTM objectives.

• Proactively identify and analyze key metrics related to prospect engagement and AI model performance to support deal closures and provide data-driven recommendations for sales strategies.

• Champion team culture by organizing and participating in team-building activities, fostering a collaborative and supportive environment for all team members.

📝 Enhancement Note: The responsibilities clearly align with supporting the sales cycle and product development through data analysis. The focus on "tuning AI models" for sales demos and improving "recommendation & search systems" based on "user behavior" highlights a critical GTM enablement function.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Data Science, Statistics, Economics, or a related quantitative field is typically expected for this level of analysis and tool development.

Experience: A minimum of three years of professional experience in data analysis, business intelligence, or a related role, with a demonstrated ability to impact business outcomes. Experience within the e-commerce or SaaS industry is highly advantageous.

Required Skills:

• Proficiency in SQL for complex data querying, manipulation, and analysis.

• Strong command of Python for data analysis, including libraries such as pandas, NumPy, and data visualization tools (e.g., Matplotlib, Seaborn).

• Expertise in BI tooling for data analysis and building dashboards for both technical and non-technical audiences (e.g., Tableau, Power BI, Looker, or similar).

• Excellent communication and interpersonal skills, with the ability to translate complex technical concepts and data findings to stakeholders with varying levels of technical understanding.

• Proven ability to manage multiple tasks simultaneously, prioritize effectively, and work under deadlines in a fast-paced environment.

• Experience with exploratory data analysis to uncover trends, patterns, and insights from large datasets.

Preferred Skills:

• Familiarity with the big data stack, such as Spark, Presto/Athena, or Hive.

• Experience in fast prototyping and providing analytical support for initiatives in the e-commerce space.

• Understanding of AI models and their application in e-commerce, particularly in search and recommendation systems.

• Experience in user behavior analysis and its application to product improvement.

• Familiarity with community building or fostering team culture initiatives.

📝 Enhancement Note: The required skills emphasize a blend of technical data analysis capabilities (SQL, Python, BI tools) and crucial communication skills to bridge the gap between technical teams and sales stakeholders, a hallmark of effective Sales Operations roles.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of analytical tools or dashboards built to support sales or customer-facing teams, showcasing user-friendliness and impact on key metrics.

• Case studies detailing how data analysis and insights were used to drive product improvements or business strategy in previous roles, particularly in e-commerce.

• Examples of process optimization projects where data was leveraged to streamline workflows or enhance efficiency.

• Documentation or examples of how complex data findings were communicated to non-technical audiences to influence decision-making.

Process Documentation:

• Showcase examples of documented analytical processes, including data extraction, transformation, loading (ETL) concepts, and analysis methodologies.

• Provide insights into how you would approach building a new analytical tool or dashboard from requirements gathering to deployment and iteration.

• Illustrate your approach to user behavior analysis, including data exploration, hypothesis generation, and validation steps.

📝 Enhancement Note: A strong portfolio demonstrating practical application of data analysis to solve business problems, particularly those impacting sales enablement and product strategy, will be critical. The ability to showcase the impact of analytical tools and data-driven insights on business outcomes is paramount.

💵 Compensation & Benefits

Salary Range: $80,000 - $120,000 USD per year. This range is based on the provided information and reflects typical compensation for a Data Analyst with 3+ years of experience in a fully remote role, considering the company's U.S. base and the need to support a U.S.-based sales team across different time zones.

Benefits:

• Unlimited vacation time: Encourages a healthy work-life balance, allowing for at least three weeks of leave per year.

• Fully remote team: Offers flexibility to work from any location.

• Work from home stipend: Provides resources for setting up an effective home office environment.

• Apple laptops provided: Ensures employees have high-quality hardware for their work.

• Training and development budget: Supports continuous learning and skill enhancement with an annual budget.

• Maternity & Paternity leave: Provides comprehensive support for new parents.

• Regular team offsites: Facilitates in-person connection and collaboration among team members.

Working Hours: The role requires availability between 7 am and 3 pm Pacific Time (PT) to support the U.S.-based sales team. This translates to a significant portion of the day needing to overlap with U.S. business hours, requiring careful time management for individuals in Argentina.

📝 Enhancement Note: The salary range is competitive for a mid-level data analyst role, especially in a remote capacity. The requirement to align with Pacific Time business hours is a key consideration for candidates in Argentina, impacting their daily schedule. The benefits package is robust, emphasizing employee well-being and professional development.

🎯 Team & Company Context

🏢 Company Culture

Industry: E-commerce Technology / AI & Machine Learning. Constructor operates in the highly competitive e-commerce space, providing AI-powered solutions for search and discovery, aiming to optimize critical business metrics like revenue, conversion rates, and profit for their clients.

Company Size: Constructor is a U.S.-based company founded in 2019. While specific employee numbers aren't detailed, the description suggests a growing, tech-focused organization with a significant engineering department. The company size implies a dynamic environment where individual contributions can have a substantial impact.

Founded: 2019, by Eli Finkelshteyn and Dan McCormick. This relatively young company history suggests a startup or scale-up environment, characterized by rapid innovation, agility, and a focus on cutting-edge technology.

Team Structure:

• The role likely sits within a broader data science, engineering, or possibly a dedicated sales operations analytics team.

• Collaboration is expected with Account Executives (sales), Frontend Engineers (product development), and potentially product managers.

Methodology:

• Data-driven decision-making is central, with a focus on metrics that matter, such as revenue and conversion rates.

• Continuous improvement and innovation, particularly in AI and LLM technologies, are core to their approach.

• Emphasis on building proprietary technology and maintaining a competitive edge through A/B testing and cutting-edge research.

Company Website: constructor.com

📝 Enhancement Note: Constructor positions itself as a leader in AI for e-commerce. The culture is described as passionate, problem-solving oriented, and focused on continuous improvement, empathy, and metrics. The relatively young age of the company suggests a fast-paced, innovative environment.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Mid-Level Data Analyst. In an operations context, this means contributing to the development and refinement of tools and processes that directly support sales effectiveness and product strategy. The impact is on enabling the GTM function through data insights and analytical solutions.

Reporting Structure: The candidate will likely report to a Data Science Manager, Head of Analytics, or potentially a Sales Operations Lead, depending on the internal team structure. They will work closely with cross-functional teams, including Sales (Account Executives) and Engineering.

Operations Impact: The role has a direct impact on the sales cycle by providing tools and insights that help Account Executives close deals. By fine-tuning AI models and improving product features based on user behavior, this role contributes to the company's revenue growth and market competitiveness.

Growth Opportunities:

• Deepen expertise in AI/ML applications within e-commerce, particularly in search, recommendations, and personalization.

• Develop advanced skills in data visualization and dashboard creation for complex business problems.

• Transition into more specialized roles within Data Science, Machine Learning Engineering, or Product Analytics.

• Potential to move into a leadership role within an analytics or operations team, managing projects or junior analysts.

• Gain exposure to cutting-edge AI technologies and contribute to product strategy at a growing tech company.

📝 Enhancement Note: The role offers a strong foundation in data analysis within a GTM context, with clear pathways for technical specialization or advancement into analytics leadership, particularly within the AI and e-commerce sectors.

🌐 Work Environment

Office Type: Fully Remote. This means there is no central physical office for this position.

Office Location(s): While the company is U.S.-based, this role is specifically for candidates in Argentina, allowing them to work from their preferred location within the country.

Workspace Context:

• The work environment is entirely remote, requiring self-discipline and strong organizational skills.

• Collaboration will primarily occur through digital communication tools (e.g., Slack, Zoom, email).

• Access to a home office setup is supported by a dedicated stipend, ensuring a productive remote workspace.

• The company provides Apple laptops, standardizing the hardware for employees.

Work Schedule: Employees must be available to work between 7 am and 3 pm Pacific Time (PT). This requires significant overlap with U.S. West Coast business hours, meaning the workday in Argentina will likely start very early or end in the late afternoon/early evening to accommodate this requirement.

📝 Enhancement Note: The fully remote nature is a significant perk, offering location flexibility within Argentina. However, the strict time zone requirement for supporting the U.S. sales team is a critical factor that will dictate the candidate's daily work schedule and requires careful consideration.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely a brief call with HR or a recruiter to assess basic qualifications, interest, and cultural fit.

• Technical Interview(s): Expect one or more rounds focusing on SQL, Python for data analysis, BI tools, and problem-solving scenarios. This may include a live coding session or a take-home assignment.

• Case Study/Take-Home Assignment: A common practice for data roles. This might involve analyzing a dataset, building a dashboard, or proposing solutions to a simulated business problem related to e-commerce or AI model performance.

• Behavioral/Team Fit Interview: Discussions focused on teamwork, communication, problem-solving approaches, and cultural alignment with Constructor's values (empathy, openness, curiosity, continuous improvement). This may involve meeting with potential team members or hiring managers.

• Final Interview: Potentially with senior leadership to discuss overall fit, strategic thinking, and long-term potential.

Portfolio Review Tips:

• Quantify Impact: For each project, clearly state the problem, your solution, the tools/techniques used, and the quantifiable results (e.g., "improved demo effectiveness by X%", "identified user behavior leading to Y% conversion uplift").

• Showcase Process: Highlight your analytical process, from data gathering and cleaning to analysis, visualization, and stakeholder communication.

• Tool Proficiency: Ensure your portfolio clearly demonstrates your skills with SQL, Python (pandas, NumPy), and BI tools. Include examples of dashboards or reports.

• Tailor to the Role: Emphasize projects that involved supporting sales teams, improving user experience, or analyzing AI/ML model performance, especially within an e-commerce context.

• Clarity and Conciseness: Present your work clearly and concisely. Be prepared to walk through your projects and explain your thought process during an interview.

Challenge Preparation:

• SQL & Python: Practice common SQL queries (joins, aggregations, window functions) and Python data manipulation tasks. Be ready to solve algorithmic or data-related problems.

• E-commerce Metrics: Familiarize yourself with key e-commerce metrics (conversion rate, AOV, LTV, bounce rate, etc.) and how data analysis can influence them.

• AI/ML Concepts: Review basic concepts of AI models, especially in the context of personalization, search, and recommendations. Understand how to evaluate their performance.

• Business Acumen: Be prepared to discuss how data analysis can drive business value and support sales goals. Think about how to translate data insights into actionable business recommendations.

📝 Enhancement Note: Candidates should prepare to demonstrate not only technical proficiency but also the ability to apply data analysis to solve real-world business problems, particularly those that directly support sales enablement and product enhancement in the e-commerce space. A strong portfolio is key.

🛠 Tools & Technology Stack

Primary Tools:

• SQL: Essential for data extraction and manipulation across various database systems.

• Python: Core for data analysis, scripting, and potentially building analytical tools. Libraries such as pandas and NumPy are critical for data wrangling and manipulation.

• BI Tooling: Proficiency in tools like Tableau, Power BI, Looker, or similar platforms for creating dashboards and reports for technical and non-technical users.

Analytics & Reporting:

• Data Visualization Libraries: Matplotlib, Seaborn (within Python) for creating charts and graphs.

• Dashboarding Platforms: Specific BI tools mentioned above.

• Exploratory Data Analysis (EDA) Tools: Python environment (Jupyter Notebooks, Google Colab) used in conjunction with pandas and NumPy.

CRM & Automation:

• While not explicitly mentioned as direct tools for this role, familiarity with CRM systems (like Salesforce) and marketing/sales automation platforms can be beneficial for understanding the broader sales ecosystem.

• Big Data Stack (Preferred): Experience with technologies like Spark, Presto/Athena, or Hive indicates familiarity with handling large-scale datasets, which is crucial for Constructor's operations.

📝 Enhancement Note: The primary focus is on core data analysis tools (SQL, Python) and BI platforms for visualization and reporting. Familiarity with big data technologies is a plus, suggesting the company works with substantial data volumes.

👥 Team Culture & Values

Operations Values:

• Empathy: Understanding and sharing the feelings of others, particularly colleagues in sales and engineering, to foster better collaboration.

• Openness: Encouraging transparency in communication, data sharing, and feedback.

• Curiosity: A drive to explore data, ask questions, and seek new solutions to challenges.

• Continuous Improvement: Commitment to refining processes, tools, and personal skills to achieve better outcomes.

• Support for Others: Actively contributing to a positive team environment and helping colleagues grow.

• Metrics-Driven: A focus on using data to measure performance and guide decision-making.

Collaboration Style:

• Cross-functional collaboration is key, working closely with Account Executives to understand their needs and with Frontend Engineers to implement solutions.

• Open communication channels are likely encouraged to facilitate the sharing of insights and feedback between departments.

• A proactive approach to identifying and solving problems collaboratively is expected.

📝 Enhancement Note: The company culture emphasizes collaboration, data-driven insights, and a supportive team environment, which are all crucial for a role that bridges technical data analysis with sales enablement functions.

⚡ Challenges & Growth Opportunities

Challenges:

• Time Zone Alignment: Working effectively with a U.S.-based team while located in Argentina requires careful scheduling and communication to ensure seamless support and collaboration across significant time differences.

• Balancing Sales Support and Product Insights: Juggling the immediate needs of the sales team (demo tools, AI tuning) with longer-term product improvement initiatives based on user behavior analysis.

• Rapidly Evolving AI Landscape: Staying current with advancements in AI and LLMs to effectively fine-tune models and suggest product improvements.

• Data Complexity: Working with massive datasets and complex AI models requires strong analytical rigor and the ability to extract meaningful insights.

Learning & Development Opportunities:

• Specialized AI/ML Skills: Gaining hands-on experience tuning and analyzing AI models relevant to e-commerce search and recommendations.

• Advanced Data Analytics: Deepening expertise in Python for data science, big data technologies, and sophisticated BI dashboarding.

• Product Development Acumen: Understanding user behavior and translating it into product feature improvements.

• Cross-Functional Communication: Honing skills in communicating technical data insights to non-technical sales and product stakeholders.

• Career Progression: Opportunities to move into Senior Data Analyst, Data Scientist, or even Sales Operations leadership roles as the company grows.

📝 Enhancement Note: The role presents opportunities to develop highly sought-after skills in AI, e-commerce analytics, and cross-functional enablement, with clear growth paths within a dynamic tech company. Managing the time zone difference is a practical challenge that requires proactive planning.

💡 Interview Preparation

Strategy Questions:

• "How would you design a dashboard to help Account Executives track the performance of their AI model demonstrations with prospects?" (Focus on metrics, user experience for AEs, and actionable insights.)

• "Describe a time you used data to identify an opportunity for product improvement. What was your process, and what was the outcome?" (Highlight exploratory data analysis, hypothesis testing, and impact.)

• "Imagine a prospect is hesitant about our AI's performance. How would you use data to address their concerns and build confidence?" (Focus on data presentation, communication, and demonstrating value.)

Company & Culture Questions:

• "What interests you most about Constructor's AI-powered approach to e-commerce search and discovery?" (Showcase understanding of their technology and market position.)

• "How do you embody Constructor's values of empathy, openness, and continuous improvement in your work?" (Provide specific examples.)

Portfolio Presentation Strategy:

• Narrative Structure: For each portfolio piece, tell a story: the business problem, your analytical approach, the tools used, the key findings, and the business impact.

• Visual Clarity: Use clear, well-designed charts and dashboards. Ensure your visualizations are easy to understand and highlight key insights effectively.

• Quantifiable Results: Emphasize the measurable impact of your work. Use numbers and percentages to demonstrate the value you brought to previous projects.

• Technical Depth: Be prepared to discuss the technical details of your analysis, including SQL queries, Python code, and the logic behind your dashboard designs.

• Relevance to Role: Connect your portfolio projects back to the specific responsibilities of this Data Analyst role at Constructor, particularly in supporting sales and improving product features.

📝 Enhancement Note: Preparation should focus on demonstrating a blend of technical data skills, business acumen relevant to e-commerce and AI, and strong communication abilities to support the sales function. The portfolio is a critical component.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Workable link.

• Tailor your resume: Highlight experience with SQL, Python, BI tools, and any relevant e-commerce or AI/ML projects. Quantify achievements wherever possible.

• Prepare your portfolio: Curate 2-3 key projects that showcase your data analysis, dashboarding, and problem-solving skills, especially those related to sales enablement or product enhancement. Ensure you can clearly articulate the impact of your work.

• Research Constructor: Understand their AI technology, client base (Sephora, Under Armour, Petco), and company mission. Familiarize yourself with their values.

• Practice time zone management: Consider how you will structure your workday to effectively support the U.S. sales team within the specified Pacific Time hours. Be ready to discuss this during the interview.

⚠️ 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.