Technical Product Manager

ABOUT US

DataWeave provides Digital Shelf Analytics and Dynamic Pricing solutions to digital commerce businesses, enabling them to grow revenues and compete profitably. Powered by AI, our platform aggregates and analyzes billions of publicly available data points from the Web to deliver easily consumable, actionable intelligence. We help businesses develop data-driven strategies and make smarter decisions.

JOB DESCRIPTION

Products@DataWeave

We, the Products team at DataWeave, build data products that deliver timely, actionable insights at scale. Our guiding principles are scale, impact, engagement, and visibility. We help businesses make data-driven decisions every day and also offer long-term strategic insights to drive their success. Our product suite includes Digital Shelf Analytics, Pricing Intelligence, Content Optimization, and SKU Management, among others, which create significant value for our customers.

We are also responsible for internal products and tools that help the rest of the organization scale. For example, we build and maintain internal tooling used by other teams. These internal users are our customers too.

Responsibilities

Own and drive multiple product modules end-to-end – including Digital Shelf Analytics, Pricing Intelligence, Content Optimization, SKU Management, and internal tools – ensuring each achieves its objectives and delivers value.

Develop a clear vision, strategy, and roadmap for each product module you own, in collaboration with the leadership team.

Detail out market requirements for current and future products by conducting market research, with input from Customer Success and Sales teams and regular customer interactions.

Develop and implement a company-wide go-to-market plan for new features/products; work with multiple functions (Marketing, Sales, etc.) to execute it effectively.

Collaborate closely with engineering teams to ensure technical feasibility and optimal implementation of new features. Understand and advocate for the latest technology trends affecting the industry.

Manage the entire product life cycle from planning to execution: prioritization, release cycles, quality and timeliness of delivery, customer engagement, and feedback loops.

Clearly articulate the value proposition of the product and all its features to help our Sales and Customer Success teams succeed.

Work with our customers to understand their pain points, be their champion internally, and prioritize requirements that create impact.

Work with UX/UI designers to ensure our products are user-friendly and meet the high standards of our customers. Incorporate feedback from user testing and customer interactions to continually improve the product.

Maintain comprehensive product documentation and update stakeholders with regular reports on progress and performance.

Manage expectations across teams and ensure our work remains aligned with the product strategy, even as we build stuff at a rapid pace.

Balance building new features with routine product support activities. Identify opportunities for automation and define processes to improve support efficiency.

Collaborate with Data Science and Data Engineering teams to productize proven AI/ML capabilities, integrate robust data pipelines, identify new data-driven product opportunities, and improve engineering processes.

Lead the conception and evolution of AI wrappers across product modules, enabling natural language querying (NLQ) and conversational workflows over product data and analytics.

Define the user experience and technical requirements for NLQ features (intent handling, clarification, guardrails, evaluation metrics) and partner with engineering to deliver performant, reliable experiences.

Work with data engineering to ensure data pipelines are AI-ready: schema consistency, freshness SLAs, observability, and metadata that improve retrieval, context, and grounding.

Drive iterative prompt and system design with rigorous evaluation (offline metrics and live A/Bs), error analysis, red-teaming, and feedback loops to continuously improve accuracy and safety.

Establish product analytics for AI features (helpfulness, resolution rate, latency, deflection, satisfaction), and translate findings into roadmap priorities.

Champion responsible AI practices, including privacy-by-design, transparency in model behavior, and compliant data use; document assumptions, limitations, and user-facing guidance.

Stay on top of emerging AI capabilities and tools; run lightweight experiments and proofs-of-concept to inform build-vs-buy decisions and accelerate delivery.

You Have

3–5 years of product management experience, including building and shipping products, preferably in a technology-focused SaaS environment (experience with data or AI-driven products is a strong plus).

Hands-on familiarity with AI product concepts such as prompt and system design, grounding with enterprise data, evaluation techniques, and iterative improvement cycles.

Experience working with or around data pipelines and retrieval patterns; able to partner with engineers to ensure reliable data flow, freshness, and observability for AI-powered features.

Comfort collaborating with engineers and data scientists on technical trade-offs (latency vs. accuracy, cost vs. quality, online vs. batch workflows) and translating these into clear product decisions.

A solid technical background with an understanding of software development and web technologies; able to read pseudo-code, write precise requirements, and reason about APIs and integration patterns.

Ability to think from first principles and at the right level of abstraction. Strong problem-solving skills (both technical and non-technical).

Excellent written, verbal, and presentation communication skills; strong at documentation of product specs, evaluation plans, and customer-facing guidance.

Great teamwork and collaboration skills, with the ability to motivate and drive cross-functional teams in a fast-paced, startup-like environment.

Curiosity about new AI technologies and toolkits; motivated to prototype, learn quickly, and bring clarity to ambiguous spaces.

Naturally customer- and impact-focused; comfortable defining and tracking success metrics for AI features (e.g., task completion, time-to-answer, accuracy, satisfaction).

Growth @ DataWeave

Fast-track growth opportunities at dynamically evolving start-ups.

Work on a variety of challenging data/design/user problems. Make a real impact.

A culture of innovation and continuous learning.

Learning opportunities with courses and product conferences.

How we Work

For us, it's hard to tell what we love more – problems or solutions! Every day, we tackle some of the hardest data, design, and user experience problems out there. We are in the business of making sense of messy public data on the Web, at serious scale