Staff Product Manager, Data & AI Collaboration Ecosystem

As a Staff Product Manager at Databricks, your primary responsibilities include leading the product strategy for the Data & AI collaboration ecosystem, defining the product roadmap for Delta Sharing, Databricks Marketplace, and Clean Rooms, and working closely with engineering, sales, and partnerships teams. You’ll drive product adoption and develop a comprehensive understanding of customer needs within the data collaboration landscape.

To be considered for the Staff Product Manager position at Databricks, you should have at least 7 years of experience managing technically complex enterprise products. A strong understanding of collaboration and sharing ecosystems is essential, along with proven success in developing and launching new products to the market.

The Staff Product Manager plays a crucial role in defining and executing product strategies for data sharing initiatives such as Delta Sharing and Databricks Marketplace. This involves articulating the product vision, collaborating with engineering teams to build features, and ensuring that the product aligns with business objectives and customer needs.

Databricks is committed to creating a diverse and inclusive culture that empowers employees. As a Staff Product Manager, you’ll have the opportunity to work with a team of innovators in the data and AI space, shape pioneering products, and influence the future of data collaboration while enjoying competitive compensation and a range of employee benefits.

The pay range for a Staff Product Manager at Databricks in Seattle is between $129,000 and $228,000 USD. This range reflects various factors such as job-related skills, depth of experience, and work location, ensuring fair and equitable compensation practices throughout our organization.

To prioritize features effectively, I examine customer feedback, market trends, and organizational goals. By using frameworks like the MoSCoW method (Must have, Should have, Could have, Won't have), I ensure that development resources are allocated to features that deliver the greatest value and align with our strategic objectives.

Certainly! In my previous role, I successfully launched a data collaboration tool that integrated user feedback from early adopters. I coordinated with engineering, marketing, and customer support to ensure a cohesive launch, resulting in a significant increase in user engagement and a 20% uplift in sales within the first quarter post-launch.

I believe communication is critical when working with cross-functional teams. I regularly schedule check-ins, ensure goal alignment, and foster an open environment for discussing ideas and addressing challenges. This keeps everyone informed and working towards a common objective, ultimately streamlining the product development process.

Gathering user feedback involves implementing surveys, conducting interviews, and leveraging usability testing sessions. I analyze this feedback to identify trends and pain points, ensuring that the insights strongly influence our product roadmap and feature prioritization.

Key metrics include user adoption rates, customer satisfaction scores, Net Promoter Score (NPS), and revenue growth. Additionally, I analyze engagement metrics like daily active users and feature utilization, which provide insights into how well the product meets customer needs.

One challenge I faced was a significant product delay due to technical issues. To tackle this, I organized a series of workshops with engineering to identify and address bottlenecks. By enhancing communication and setting realistic timelines, we were able to resolve the issues and successfully launch the product shortly after.

I actively follow industry blogs, attend conferences, and participate in webinars related to data and AI. Networking with peers and engaging in professional organizations also helps me stay informed about the latest advancements and market shifts, which I can leverage in product strategy.

Market research is a cornerstone of my product strategy. By understanding competitors, industry trends, and customer needs, I can make informed decisions about product positioning, feature development, and pricing strategies. This helps ensure our offerings remain competitive and relevant in the market.

Success post-launch can be defined through various metrics, such as user retention, positive feedback, and meeting set KPIs. I also look at the product's impact on revenue and customer satisfaction, along with the ability to iterate on user feedback to enhance the product further.

I am excited about the opportunity to work at Databricks due to its commitment to innovative data solutions and its collaboration-focused culture. I believe my background in leading technically complex products aligns well with the role, and I'm eager to contribute to a team dedicated to reshaping the data and AI landscape.