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
- Partner with Account Executives to qualify mutually successful opportunities and guide the opportunity through a successful technical win
- Develop and lead the technical strategy of an account from prospect to deal closure, along with a path to implementation success
- Tailor customer presentations, demos, and managed trials that map to the discovered requirements for the customer
- Proactively identifying potential blockers and circumventing or developing alternate paths toward a solution
- Nurture relationships, building technical champions within accounts
- Active participant in driving product feedback, influencing the future of Tecton products
- This role requires some travel to customer sites and as needed for company-related events
Qualifications
- This role must be based within the Greater London area
- 4+ years of experience in a technical sales or consulting capacity with enterprises, focusing on complex solution sales of mission-critical data systems (databases, data warehouses, big data systems, analytics, and/or machine learning)
- Deep technical understanding of data and ML tooling, workflows, and trends in enterprise setting
- Proficiency in Python, Spark, and/or SQL with experience developing ETL applications
- Excellent communication and presentation skills, with comfort in objection handling
- Hands-on experience with AWS
- Databricks and/or Snowflake experience a plus
This role is eligible to participate in Tecton’s Commission Plan. Individual compensation packages are based on multiple factors such as location, level, role scope, and complexity, as well as additional job-related factors such as skills, experience, and expertise.
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].
Top Skills
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.
.png)
.png)
