Product Manager - Data & Ecommerce

Mission

Own and execute major portions of Palazzo's product roadmap — driving the convergence of AI, data analytics, and commerce. Translate vision into reality by guiding a small development team with data-driven insights, technical depth, and ecommerce expertise.

Outcomes (12–18 Month Goals)

Own and deliver major roadmap initiatives

• Take ownership of one or more core product areas (e.g. Analytics Platform, Shoppability Engine, Conversion Optimization).

• Deliver multiple high-impact launches that move core KPIs (e.g. SKU clickthrough, conversion rate, average order value, user engagement).

Operate as player-coach across a small dev team

• Define clear product specs grounded in data analysis and success metrics for each release.

• Help unblock developers with quick decisions, data-backed recommendations, and clear documentation.

• Maintain sprint discipline while fostering data-driven experimentation.

Build and scale data infrastructure

• Design and implement analytics frameworks to measure product performance across the funnel.

• Partner with engineering to define event taxonomies, data pipelines, and reporting dashboards.

• Establish automated reporting that surfaces insights to leadership and the broader team.

Drive ecommerce performance

• Obsess over conversion metrics: clickthrough rates, add-to-cart, checkout completion, and revenue per session.

• Run A/B tests and multivariate experiments to optimize user journeys and merchandising strategies.

• Identify and eliminate friction points in the purchase funnel using quantitative and qualitative data.

Integrate AI into analytical workflows

• Use AI daily for data analysis, pattern recognition, forecasting, and insight generation.

• Drive adoption of AI tools internally for automated reporting, anomaly detection, and predictive analytics.

• Champion AI-powered personalization and recommendation features in the product.

Competencies

Data & Analytics Mastery: Expert in SQL, comfortable with Python/R for analysis, experienced with BI tools (Looker, Tableau, Mode). Can design instrumentation plans, build dashboards, and translate complex data into actionable insights.

Ecommerce Fluency: Deep understanding of ecommerce metrics (CAC, LTV, conversion funnels, basket analysis, merchandising KPIs). Has shipped features that directly impacted revenue and retention.

Technical Product Sense: Understands APIs, backend workflows, data structures, and AI pipelines to define realistic requirements and collaborate effectively with engineers.

AI-Forward Thinking: Uses AI tools for data analysis, forecasting, and optimization; understands how to leverage LLMs and ML models to surface insights and drive product decisions.

Experimentation Rigor: Designs and analyzes A/B tests with statistical rigor; understands sample sizes, significance testing, and incrementality measurement.

Execution Mastery: Acts as a player-coach who balances strategic ownership with sprint management, keeping team velocity high through clear prioritization.

Strategic Thinking: Connects product work to business outcomes; sees how each feature supports revenue growth, margin improvement, and competitive positioning.

Leadership & Communication: Translates data into compelling narratives; earns respect from technical teammates through analytical clarity and conviction.

Cultural Attributes

• Data-native — uses data instinctively to inform every decision; comfortable diving into raw datasets.

• Ecommerce-obsessed — lives and breathes conversion metrics, customer behavior, and merchandising strategy.

• AI-augmented — leverages AI tools to accelerate analysis and uncover hidden patterns.

• Outcome-driven — measures success by what ships and how it performs against KPIs.

• Builder's mindset — pragmatic, curious, and comfortable wearing multiple hats in a startup environment.

Background & Skills

• 4–8 years in Product Management, ideally at an ecommerce startup, marketplace, or retail tech company.

• Strong SQL skills (required) — can write complex queries with joins, CTEs, window functions.

• Proficiency with analytics platforms: Mixpanel, Amplitude, GA4, Segment, or similar.

• Experience with BI tools: Looker, Tableau, Mode, Metabase, or similar.

• Ecommerce platform knowledge: Shopify, Magento, custom commerce stacks, payment gateways, inventory systems.

• A/B testing expertise: Designed and analyzed experiments; understands statistical significance and incrementality.

• Working knowledge of Python or R for data analysis (bonus).

• Experience shipping data products or analytics features (dashboards, reporting tools, recommendation engines).

• Understands how APIs, databases, and ML model systems work at a technical level.

• Has worked closely with small development teams (<10 people) in a fast-paced environment.

• Bonus: Experience with prompt engineering, LLM integration, or generative model applications in ecommerce.

Success Metrics

Metric

Target

% of roadmap delivered on time

≥ 90%

Key KPI improvement (conversion, AOV, engagement)

+20% in owned areas

Data instrumentation coverage

≥ 95% of features

Experiment velocity (tests shipped per quarter)

≥ 8

Developer satisfaction / clarity rating

≥ 8/10

Cross-functional satisfaction (eng/data/execs)

≥ 8/10

Time-to-insight (dashboard availability post-launch)

≤ 3 days