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.
As a Consulting Architect on Tecton's professional service team, you’ll provide technical and consultancy expertise to assigned customers to implement Tecton on-time while achieving their desired outcomes. You’ll work closely with them to successfully implement Tecton deployments and build features, in both an advisory and/or hands-on fashion, to enable their high-impact AI/ML use-cases.
Responsibilities
- Collaborate with the Delivery/Engagement Managers, pre-sales, and Account team to understand customer needs and business requirements/objectives
- Design, architect, and implement Tecton that will be setup for success in production
- Contribute to the creation of a project plan, that includes detailed steps and timelines, helping provide visibility internally and externally to the status of the project
- Drive consensus on technical decisions quickly and effectively, while also keeping timeline and scope-of-work on track
- Be an expert in product, driving material impact and contributions back to the business. As an expert in the field, the role will have high levels of influence into customers needs and potential product improvements
Qualifications
- 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 engineering and AI/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, and other cloud providers
- Databricks and/or Snowflake experience a major plus
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.
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.
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