About us
At Dagster Labs, our mission is simple but ambitious: to empower every organization to build a productive, scalable data platform. We’re the brains behind Dagster, the open-source tool developers love, and Dagster+, our managed cloud. From scrappy startups to global enterprises, thousands of teams trust us to power the data behind their analytics, machine learning, and AI breakthroughs.
We believe the AI era runs on reliable, high-quality data. That’s why we make data platforms easier to test, easier to understand, and actually enjoyable to use. Dagster is quickly becoming the backbone of how the world builds intelligent systems.
We’re a small, well-funded team with big energy and a track record of shipping software people rave about. We aim high and work as a team, holding ourselves to the highest standards and trusting eachother to deliver. We care about how we work together: no egos, no drama, just a lot of curiosity and a drive to make something meaningful.
If you want to help shape the future of data and AI and have fun doing it, come build with us.
About the role
Dagster is building a new function at the intersection of data, strategy, and sales execution. As our first GTM Engineer, you'll partner with our Enterprise sales team to turn data insights into coordinated, high-impact account strategies that land and expand our largest deals.
This isn't a traditional sales ops or marketing role. Reporting into Analytics, you'll bring technical rigor and data-driven frameworks to Enterprise GTM: building the playbooks, providing strategic direction for campaigns, and creating the connective tissue between Sales, Marketing, Product Marketing, and leadership that turns opportunities into wins.
You'll work embedded with Enterprise Account Executives, turning territory planning from art into science, and ensuring every strategic account gets the coordinated, multi-channel attention it deserves. You'll use SQL to pull account data, analyze pipeline, and build prioritization models, bringing systems thinking to territory-level demand generation.
This is a full-time position offering competitive salary, equity, and benefits. We are a distributed team with offices in San Francisco, New York, and Minneapolis. We’re open to hiring fully remote candidates who are currently authorized to work within the United States. We offer flexible remote work options so you can choose the environment that makes you most productive—whether that’s your home or a coworking space. Dagster Labs fosters a collaborative, remote-first culture, ensuring you have all the tools and support needed to thrive, no matter where you are.
Responsibilities:
Systems & Insights
Pull and analyze data from Salesforce, HubSpot, Clay, Common Room and other market intelligence tools to inform strategy
Create territory reports and prioritization models that scale
Build repeatable templates for campaigns, account plans, and reporting
Work with RevOps to develop workflows across Clay, LinkedIn Sales Navigator, ZoomInfo, and other prospecting tools
Build and maintain workflow automations using n8n or similar tools to streamline data flows and repetitive processes
Territory & Account Strategy
Build data-driven frameworks for account prioritization based on revenue potential, strategic value, buying signals, and product fit
Conduct whitespace analysis and expansion mapping for named accounts
Create alerting and monitor trigger events (funding rounds, hiring patterns, tech stack changes, executive moves) to identify in-market opportunities
Campaign Planning & Strategic Direction
Partner with marketing and sales to develop account-specific messaging and positioning tailored to persona and buying stage
Create GTM playbooks for Enterprise motions (new business and named account expansion)
Provide strategic direction for focus campaigns on key pursuits; defining approach for LinkedIn ads, custom landing pages, executive dinners, and industry events (execution handled by Marketing and Sales teams)
Must-have Qualifications
4-6 years in sales, marketing, RevOps, or enablement with exposure to complex Enterprise B2B sales cycles
Experience with GTM tools (our stack includes HubSpot, Salesforce, Clay, and Common Room, but you don’t need to know them all Day 1)
SQL fluency to pull account lists, analyze pipeline, and build prioritization models in Snowflake and Salesforce
Proven ability to design account-specific, multi-channel strategies and translate them into execution briefs
Systems thinking: ability to see patterns, build frameworks, and create repeatable processes
Experience balancing competing priorities and building trust with sales teams in matrix organizations
Nice-to-have Qualifications
Experience in technical or developer tools
Python experience
Familiarity with market intelligence platforms (LinkedIn Sales Navigator, ZoomInfo, 6sense)
Background building 0→1 functions or processes
Understanding of data orchestration, modern data stacks, or technical buyer personas
You belong here
We are committed to building an inclusive team and an open-source community where no one feels out of place. We know that teams with diverse backgrounds state their assumptions more explicitly, think more rigorously, and build better software. Plus it's more fun and interesting to work with a wide variety of perspectives.
You should apply to work at Dagster Labs if you want to work in, and help to build and strengthen, a high-performing software development environment where people of all backgrounds are welcome.
Our Stack
Dagster is built in Python and TypeScript, and runs seamlessly across macOS, Linux, and Windows. Our frontend is powered by React and GraphQL, delivering a modern, intuitive developer experience. We value engineers who are comfortable working across the stack and making thoughtful contributions.
The estimated OTE range for this role is $135,000 to $180,000.
Other resources:
The launch of Dagster blog post
Launching Dagster Cloud to GA blog post
Dagster’s Data Platform Week release in Nov 2024
Software-Defined Assets blog post, Data Council talk, and animated explainer
Our latest episode on the Data Engineering Podcast
Our philosophy on code reviews