Here’s a summary of the role:   

You’re ready to work at scale — re‑thinking how data is collected, processed and delivered to our customers end‑to‑end. As a Staff Software Engineer working on a  building up a cutting edge data ecosystem, you’ll shape architectures that power feeds, analytics platforms, reports and dashboards used by customers every day. You’ll explore and apply cloud technologies, distributed systems, AI solutions, influencing technical direction while still getting hands‑on with code. This role is for you if you thrive on data pipelines, APIs, cloud‑native services, scalability, performance, and reliability, enjoy technical leadership, and want your engineering decisions to have visible impact from ingestion to insight. 

Here’s a breakdown of what you’ll do (not all of it, just the important stuff): 

• Set technical direction across teams, driving ai-native and cloud‑native architecture and long‑term system evolution 

• Lead design of secure, scalable MCP hosts, and AWS microservices, balancing performance, reliability, and cost 

• Influence AI adoption through AI first solution desing, model guidance, prompt strategy, and built‑in governance and monitoring 

• Shape roadmaps with Product, Security, and DevOps to deliver compliant, high‑impact solutions 

• Evaluate and pilot emerging technologies to boost developer productivity and platform capability 

• Mentor engineers, raising engineering standards and fostering ethical, AI‑aware practices 

These are the essentials you’ll need to get an interview: 

• Deep background in enterprise software design, implementation, and operation (OOP, commercial environments) 

• Extensive hands‑on experience with cloud native architecture and services, ideally on AWS 

• Strong foundation in data driven systems: data acquisition, processing pipelines, and customer facing reporting 

• Proven experience with delivering AI based customer solutions to production 

• Familiarity with modern data transformation tools such as dbt 

• Proven expertise with SQL and NoSQL databases, alongside APIs and microservices at scale 

• Senior engineer mindset: proactive problem solver, comfortable owning outcomes 

• Leading multiple concurrent projects and ensuring strong collaboration with a wide range of stakeholders 

• Passion for continuous improvement—sharing knowledge, refining processes, and raising the bar across teams 

• Curiosity for new tools and patterns, with a pragmatic approach to adopting what works best 

 It would be great if you had these to, but we’ll support you if you don’t: 

• Experience with TypeScript and Python in production, ideally across data‑ and service‑oriented systems 

• Hands‑on experience with PostgreSQL and strong SQL workflows 

• Exposure to cloud data platforms, especially Snowflake, at scale 

• Comfortable working across the full data lifecycle—from ingestion to analytics and reporting 

#LIHybrid

This role is located in Budapest, Hungary — We are a hybrid work environment and are in the office 3+ days/per week.

Tulip, the leader in AI-native frontline operations, is helping companies around the world equip their workforce with composable, connected apps, leading to higher quality work, improved efficiency, and end-to-end traceability across operations. Tulip's cloud-native, no-code platform, powered by embedded AI, is driving the digital transformation of industrial environments through composable, human-centric solutions that go beyond disrupting the Manufacturing Execution System (MES) category.

A spinoff out of MIT, Tulip is headquartered in Somerville, MA, with offices in Germany, Hungary, Singapore, and Israel. Tulip has been recognized as a World Economic Forum Global Innovator, a 2024 Deloitte Technology Fast award winner, one of Energage's Top Workplaces USA, and one of Built In Boston's "Best Places to Work" and "Best Midsize Places to Work."

About You

• You've built production data models and know how to turn vague business questions into clear, canonical definitions
• You use AI tooling as your primary mode of engineering and have developed a real point of view on how to deliver high-quality datasets when the technical work is done by AI
• You're comfortable engaging with stakeholders to untangle what they actually need and turning that conversation into a structured analytical approach
• You're looking for a role where agentic tools are the default, and where you can help shape how a central data team delivers the reliable signals that operational and strategic decisions depend on.

What Skills Do I Need?

• Bachelor's degree in a quantitative, technical, or business field, or equivalent working experience.
• 4+ years of experience in analytics, data science, or data engineering, with increasing scope and ownership.
• Proficiency with agentic coding tools (e.g., Claude Code, Cursor) as your primary mode of building, testing, and documenting production data models, including shipping reusable assets that improve agentic workflows.
• Experience translating business requirements into structured analytical approaches, running workshops, and aligning stakeholders on canonical metric definitions.
• Strong data modeling skills and the ability to write performant SQL against cloud warehouses, with a strong emphasis on data quality and automated testing.
• SQL and Python proficiency for data modeling, pipeline orchestration, and API integrations using the broader data stack (dbt, Airflow, dlt, etc.).

Nice to Haves

• Experience with semantic modeling tools such as MetricFlow, Cube, or LookML
• Experience with data cataloging and metadata platforms such as DataHub or Atlan
• Familiarity with BI tools such as Tableau, Looker, or Sigma

Key Responsibilities

• Build and maintain data models and pipelines primarily using AI coding tools, with consistent best-practice patterns, clear documentation, and high-quality standards
• Build and improve agentic coding workflows to accelerate data modeling and testing, while staying in the loop on business logic, review, and company-specific edge cases
• Partner with stakeholders to surface conflicting metric definitions, run alignment workshops, and establish canonical standards across domains
• Define and enforce data quality standards across pipelines — including unit tests grounded in sample data and lineage-aware test placement — and enable domain analysts to follow the same standards
• Prototype and iterate on a semantic and context layer that makes Tulip's data accessible to both analysts and AI agents, including the documentation and instructions that let an LLM behave correctly against it
• Help the team make sensible calls about where on the risk curve to deploy AI: lower-risk for internal modeling and testing, more cautious for self-serve BI, and most cautious for anything customer-facing

Key Collaborators

• Commercial Teams
• Marketing Teams
• Product and Engineering Teams
• Operational Leadership
• Data Analytics stakeholders across departments

Working at Tulip

We know even great candidates experience imposter syndrome. Even if you don't match every requirement, applying gives you the opportunity to be considered.

We're building a strong, diverse team that values hard work, families, and personal well-being. Benefits of working with us include:

• Direct impact on product and culture
• Private Healthcare insurance
• Company equity
• Virtual company events and happy hours
• Flexible work schedule and unlimited vacation policy
• Team Lunches in KFT Hub and office snacks
• Learning & Development benefits
• HU Eyeglass Benefit
• An inclusive, dog-friendly office with diverse and inspiring colleagues

We are an equal opportunity employer. At Tulip, we celebrate all. Qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Help us build an inclusive community that will transform frontline operations.

Please note that we may use AI-based tools to support parts of our hiring process. All data processing is carried out in compliance with local data protection laws, ensuring all personal candidate information is handled securely and ethically.