Solutions Engineer

About the role

The role is based in Copenhagen and focuses on driving technical success for a portfolio of enterprise accounts by deeply understanding customer objectives and converting them into functional, working solutions inside the Subsets platform. You will operate at the intersection of product implementation and data engineering, constructing and validating machine learning features such as propensity models and churn signals while guaranteeing the integrity and reliability of data pipelines linking systems like Braze and Sailthru. This position requires you to interpret complex business requirements and assess their technical viability, rapidly prototyping novel approaches while identifying gaps in existing product capabilities and resolving any bugs that emerge. You will work in close partnership with the Lifecycle Engineer who manages the client relationship, taking full ownership of the technical execution and delivery on the customer side. The environment is designed for individuals who thrive in a fast-moving, high-impact setting where autonomy and responsibility are expected from day one. You will engage directly with the codebase, analyzing, troubleshooting, and fixing real-world customer data flows, possessing the technical capacity to potentially handle production issues even if deployment remains a separate function.

Key facts

What you'll do

• Assume ownership of the technical success of a portfolio of accounts and translate abstract customer goals into concrete, working solutions within the Subsets platform.
• Engage directly with data and models to build, validate, and deploy machine learning-driven features such as propensity models and churn signals, ensuring robust and reliable data flows across diverse integrations including Braze, Sailthru, and other connected systems.
• Act as the bridge between product vision and engineering reality by validating the feasibility of proposed use cases, rapidly prototyping new solutions, identifying missing product functionality, and taking responsibility for diagnosing and fixing bugs.
• Partner tightly with the Lifecycle Engineer responsible for the client relationship, focusing intensely on the technical aspects of the engagement while they manage the commercial and stakeholder management side.
• Operate within a lean, high-performing team structure where your individual contribution has a significant and direct impact on customer outcomes and where you assume a high degree of responsibility with minimal oversight.
• Utilize the core tech stack consisting of Python, Google Cloud Platform, BigQuery, dbt, and PostgreSQL to design, implement, and maintain scalable data solutions.
• Work at times directly within the production codebase, reading complex code, debugging intricate issues, and applying fixes to real customer data pipelines, accepting that while you may not handle production deployments by default, you must possess the technical competence to do so.
• Continuously expand your hands-on experience with data pipelines, complex integrations, and debugging difficult data quality issues that arise in live production environments.
• Apply deep familiarity with machine learning and artificial intelligence concepts, including experience working with or deploying predictive models into operational settings.
• Leverage your familiarity with CRM and marketing automation platforms such as Braze and Sailthru to ensure smooth integration and data synchronization across the marketing stack.
• Move fluidly between intense technical work involving data modeling and system architecture and clear, concise communication with non-technical stakeholders to align expectations and deliver results.
• Thrive under tight timeline constraints, managing multiple priorities and delivering high-quality technical solutions on schedule.
• Prefer a primarily in-office working arrangement that allows for rich, spontaneous collaboration and direct access to the team.
• Channel your energy into the fast pace and high intensity characteristic of early-stage startups, embracing ambiguity and contributing to rapid iteration.
• Contribute to a competitive compensation package and equity structure that reflects the value and impact of your technical expertise.

Requirements

• Bring hands-on experience with building, managing, and debugging data pipelines and integrations, specifically demonstrated through your history of resolving data quality issues in production environments.
• Show familiarity with core machine learning and artificial intelligence concepts, coupled with practical experience working with, training, or deploying predictive models in a real-world context.
• Possess familiarity with CRM and marketing automation ecosystems, with specific experience using platforms like Braze or Sailthru representing a significant advantage.
• Exhibit the ability to shift comfortably between deep, hands-on technical tasks such as writing complex SQL or debugging code and articulating technical constraints to non-technical clients in a clear manner.
• Prove you can perform effectively under strict timeline constraints, managing deadlines and delivering technical work without compromising quality.
• Indicate a preference for a primarily in-office work model, as collaboration and on-site problem-solving are central to the role.
• Demonstrate that you are energized and motivated by the dynamic, fast-paced environment of an early-stage startup, where roles evolve quickly and adaptability is essential.
• Require a competitive total compensation package that includes both salary and equity components commensurate with the scope of responsibility and impact.

Skills & tools

You will use python. You will use sql. You will use go. You will use postgres.

Practical notes

Note: own the technical success of a portfolio of accounts and translate customer goals into concrete, working solutions within the Subsets platform.

work hands-on with data and models: build, validate, and deploy ML-driven features such as propensity models and churn signals, and ensure reliable data flows across integrations including Braze, Sailthru, and others.

bridge product and engineering: validate the feasibility of use cases, prototype new solutions, identify product gaps, and take care of bugs.

partner closely with our Lifecycle Engineer(s), who lead the client relationship, while you own the technical part.

work in a lean, high-performing team where you will have a significant impact and assume high levels of responsibility.

For , Tech stack: Python, Google Cloud Platform, BigQuery, dbt, PostgreSQL.

At times, you'll work directly in our codebase, reading code, debugging, and fixing across real customer data pipelines. You won't own production deployments by default, but you should be technical enough to do so.

hands-on experience with data pipelines, integrations, and debugging data quality issues in production environments.

familiarity with ML/AI concepts and experience working with or deploying predictive models.