Data Engineering Lead

At Founderful, we focus on helping founder teams of Swiss tech startups to achieve their ambitious goals in becoming international market leaders. This also means supporting our portfolio companies in attracting and hiring the top-tier talent for critical positions.

We're now supporting one of our portfolio companies, a deep-tech ETH spin-off, on their mission to maximise the safe operational life of critical infrastructure by combining cost-efficient sensors with intelligent algorithms that translate vibrations into actionable structural health metrics.

They are bringing the digital, data-driven revolution into the built environment to enable smarter, predictive, sustainable maintenance.

As a
Data & Engineering Lead
, you will be a key technology leader in the company. You will define and own the data architecture, engineering processes, platform infrastructure and dev-ops practices at the intersection of AI, IoT, and large-scale analytics, that will scale our product from pilot to global deployment. You will build and lead a team, set standards for code quality, data governance and operational excellence, and collaborate closely with product, data science, hardware and business stakeholders to deliver our vision.

Key Responsibilities

Data Architecture

• Deep understanding of data architecture, pipelines, warehousing, and real-time data streaming.
• Solid experience and understanding of considerations for large-scale solutioning and operationalization of modern data warehouses, data lakes, and analytics platforms.

Data Engineering

• Design build and optimize real-time, highly available data pipelines, logic and storage systems with latest coding practices and industry standards (e.g., Databricks, Airflow, Spark, Kafka), while at the same time removing technical impediments
• Ensure high availability, reliability, and performance of data systems through monitoring, alerting, and operational best practices.
• Serve as the technical liaison between data infrastructure and downstream data consumers (dta science and engineering teams), ensuring alignment on definitions, metrics, and SLAs.
• Continuously evaluate and improve the technical stack and infrastructure to support evolving data and scalability needs.

DevOps

• Write unit, integration tests and functional automation, towards quality assurance
• Experience in working with DevOps automation tools & practices and CI/CD pipelines
• Continuously assess and optimize bottlenecks and opportunities to improve operational efficiency through automation and scalability

Data quality and governance

• Set standards for data quality, testing, version control, and deployment across all stages of the data lifecycle.
• Experience implementing and operationalizing data governance policies (e.g., data cataloging, metadata management, data lineage, access control).
• Experience operating in regulated environments with security and compliance needs.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Engineering or equivalent.
• 7+ years' experience in data architecture, data engineering or solutions architecture roles.
• 3+ years' formal leadership experience (team lead or higher) with responsibility for code/design reviews, mentoring engineers and driving technical strategy.
• Experience designing and building scalable data pipelines (ETL/ELT) for extraction, transformation, loading in cloud-based microservice architectures.
• Strong programming skills in Python; experience with data engineering frameworks (Databricks, Spark, Airflow) and streaming technologies (Kafka) desirable.
• Proficiency with SQL and NoSQL databases, cloud infrastructure and service-based architectures.
• Experience working with DevOps automation tools, CI/CD pipelines, and writing tests (unit, integration, functional).
• Proven experience implementing data governance, version control, testing, deployment across full data lifecycle.
• Experience in or strong understanding of regulated environments (security, compliance) is a plus.
• Excellent communication skills and ability to collaborate cross-functionally with product managers, data scientists, hardware engineers, and business stakeholders.

You are the best fit for the role if:

• You are passionate about scaling the data and technology layer of a startup from pilot to global deployment and relish building systems that make a real-world impact.
• You have proven experience taking responsibility end-to-end: architecture, build, operationalisation; you enjoy both technical depth and leadership/strategy.
• You thrive in a start-up environment: you're comfortable with ambiguity, rolling up your sleeves, shaping processes, and driving structure in a fast-moving business.
• You value high reliability, performance and operational excellence; you set high standards and lead others to meet them.
• You want to work at the intersection of software, data, AI/ML, and physical infrastructure (hardware + sensors), and you see the potential in using data to transform real assets.
• You are motivated to mentor and grow a team, set culture, define engineering practices and help build a strong technical organisation from the ground up.

You are not the best fit if:

• You prefer working in an environment where most processes are already defined and you're not expected to build much from scratch.
• You are primarily a specialist contributor (e.g., only writing code) and are not interested in leadership, architecture, strategy, or building teams.
• You are not comfortable with fast iteration, ambiguous problem-spaces, frequent pivots, or taking on broad responsibility outside a defined "data engineering" silo.
• You lack experience in building scalable, production-grade data platforms, or have not worked with real-time streaming, large-scale data architecture or operationalisation.
• You are not motivated to work in a mission-driven environment where the intersection of infrastructure, sensors, data and AI is central and the business is still early-stage.