Product Manager - Developer Workflows

At Tecton
, we solve the complex data problems in production machine learning. Tecton’s feature platform makes it simple to activate data for smarter models and predictions, abstracting away the complex engineering to speed up innovation.

Tecton’s founders developed the first
Feature Store
when they created Uber’s Michelangelo ML platform, and we’re now bringing those same capabilities to every organization in the world.

Tecton is funded by Sequoia Capital, Andreessen Horowitz, and Kleiner Perkins, along with strategic investments from Snowflake and Databricks. We have a fast-growing team that’s distributed around the world, with offices in San Francisco and New York City. Our team has years of experience building and operating business-critical machine learning systems at leading tech companies like Uber, Google, Meta, Airbnb, Lyft, and Twitter.

Drive excellence and product fit for Tecton’s primary user personas, the ML Engineer and the Data Scientist. Perform research with Tecton users and the broader market to understand and support these personas. Through both dedicated engineering resources and cross-team collaboration, build and refine the product workflows for exploring, developing, testing, and product ionizing features across Tecton’s framework, CLI, SDK, API surface and GUI.

Champion the Data Scientist and MLE personas k with Product Marketing and Dev Rel to describe, promote, and evangelize Tecton as an ideal solution for feature engineering.

• Drive product-market fit with ML Engineers and Data Scientists. Ensure Tecton is the best available tool for developing and product ionizing features for predictive machine learning. Partner extensively with Tecton internal experts and other PMs to ensure our capabilities are accessible and effective for users.

• Represent the user perspective. Maintains extensive direct customer and user contact through regular calls, implementation reviews, and support escalations. Develops customer intuition through first-hand data collection and direct observation, not filtered reports. Regularly reviews customer call recordings and documentation to spot patterns and opportunities. Cites specific customer examples when writing requirements.

• Shape product strategy and direction. Strong business acumen that extends beyond functional expertise. Contributes meaningfully to company-wide strategy and decision-making. Understands market dynamics and helps guide prioritization and requirements development. Operates as an SME for Data Scientist and ML Engineer personas and workloads.

• Support Go-to-Market. Brings expertise in target personas and workloads when supporting the development of marketing communications. Participates in demos, webinars, and content creation, adding deep insights and mature skills, representing the user and their workflows. Partner with PMM and Sales on new business activities and OKRs.

• 3-5 years in Product Management on highly-technical products

• Demonstrable experience writing PRDs and requirements for technical products and working cross-functionally with both GTM and Engineering teams

• 2+ years in Product Management at early-stage (50-150 employee) startups

• Demonstrated competency participating in webinars, briefings, customer presentations and demos

• Excellent skills in user research, outbound discovery, and connection-building. Experience prospecting on Linked In, etc for research partners who are not customers

• Operator-level experience with SQL, Python, Notebook environments, and Git. Can demonstrate working knowledge of these skills

• Familiarity with streaming and batch data engineering patterns and technologies

The estimated US base salary range for this position is $162,000 - $222,000 annually for employees based within California & New York. In addition to base salary, we offer competitive equity & comprehensive benefits such as medical, dental, vision, life, 401(K), flexible paid time off, 10 paid holidays each calendar year, sick time, leave of absence as per the FMLA and other relevant leave laws.

Individual compensation packages are based on multiple factors such as location, level, role scope, and complexity, as…