Enterprise Account Executive

Posted 9 Days Ago
Hiring Remotely in US
Remote
273K-327K Annually
Senior level
Machine Learning
The Role
The Enterprise Account Executive at Tecton will lead a product-driven sales strategy, focusing on technical solutions for data and machine learning systems. Responsibilities include managing the complete sales cycle for new accounts, conducting outreach to prospects, understanding technical challenges, and articulating Tecton's unique value proposition, thereby building strong customer relationships.
Summary Generated by Built In

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.

Responsibilities:

  • Define and drive our product led, bottoms up GTM sales motion
  • Execute on a cloud-first, data/ML-centric sales strategy
  • Run the end-to-end sales process for net-new accounts
  • Identify and contact prospects through targeted outbound activities
  • Understand customer technical challenges and existing technology stacks
  • Articulate Tecton's technical differentiation, integration within existing customer technology stacks, and value proposition
  • Build exceptional value with all engagements

Preferred qualifications:

  • At least 5+ years of experience in a technical sales capacity focused on a product-led, bottoms up GTM motion for mission-critical data systems (databases, data warehouses), big data systems, analytics, and/or machine learning
  • Deep technical understanding: Thorough understanding of data and ML tooling, workflows, and industry trends
  • Proven track record of exceeding sales quotas
  • Driven and competitive: Possess a strong desire to be successful
  • Customer relationship builder: Excellent at building deep relationships with prospects and customers
  • Great communicator: Exceptional presentation and communications skills

Tecton values diversity and is an equal opportunity employer committed to creating an inclusive environment for all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other applicable legally protected characteristics. If you would like to request any accommodations from the application through to the interview, please contact us at [email protected].


This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.

The Company
HQ: San Francisco, CA
88 Employees
Hybrid Workplace
Year Founded: 2019

What We Do

Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature store to make world-class machine learning accessible to every company.

Machine learning creates new opportunities to generate more value than ever before from data. Companies can now build ML-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes.

But ML models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage ML data. Most companies don’t have access to the advanced ML data infrastructure that is used by the internet giants. So ML teams spend the majority of their time building custom features and bespoke data pipelines, and most models never make it to production.

We believe that companies need a new kind of data platform built for the unique requirements of ML. Our goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. By getting the data layer for ML right, companies can get better models to production faster to drive real business outcomes.

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