Data Engineer

Our story

At IRMOS we are on a mission to maximize the safe operational life of existing buildings and bridges. We fundamentally improve the status quo in structural analysis and maintenance planning, by combining sensor data with intelligent algorithms. We are a dynamic team of innovative engineers, with extensive expertise in structural assessment, structural health monitoring, data science, and software development. We envision safer and more sustainable infrastructure for everybody and for this we need to clearly communicate infrastructure health metrics to various stakeholders.

The opportunity

Join us as a Data Engineer and be a part of reshaping structural maintenance through innovative data-driven solutions. Your expertise will drive data-driven structural assessment and contribute to the evolution of IRMOS in our dynamic startup environment.

What you will do:

As a Data Engineer, you will be designing, building, and maintaining scalable and efficient data pipelines, architecting distributed systems, implementing big data storage solutions, developing data integration frameworks, and collaborating with cross-functional teams to ensure seamless data flow and delivery. Your responsibilities will include:

• Data Pipelines & Orchestration: Design, build, and optimize real-time, highly available data pipelines using distributed processing frameworks. You will lead our transition into modern orchestration tools, specifically Dagster .

• Data Quality & Operations: Ensure high availability, reliability, and performance of data systems through proactive monitoring, alerting, and automated unit/integration testing.

• Database Management: Own our database infrastructure. You will maintain, optimize, query-tune, and scale our databases to handle increasing data volumes and velocity.

• API Integration: Develop and maintain robust custom API services to seamlessly fetch, ingest, and process data from a variety of third-party vendors and sources.

• Data Science Collaboration: Act as the engineering bridge for our Data Science workflows, helping to streamline data preprocessing, feature engineering pipelines, and model deployment infrastructure

through MLOPs processes and tools

.

• Architecture & Scaling: Ensure our overall data architecture is clean, thoroughly documented, and built to scale effortlessly alongside the company’s growth.

What you will bring:

• Education & Experience: Bachelor's degree in Computer Science (or a related technical field) and 3+ years of experience in Data Engineering or backend infrastructure.

• Pipeline Expertise: Proven track record of designing and building scalable ELT/ETL pipelines for complex data extraction and transformation.

• The Tech Stack: Deep proficiency in Python and experience with modern orchestration frameworks (with a strong preference for Dagster , or Airflow/Prefect and a desire to learn Dagster).

• Database: Deep knowledge of SQL and hands-on experience optimizing/scaling relational (e.g., PostgreSQL) and non-relational databases, as well as data warehousing/data lake architectures.

• Software Engineering best practices: clean code, version control (Git), CI/CD pipelines, and containerization (Docker).

• Data Science foundations: A solid foundational understanding of Data Science concepts (e.g., how data features are prepared and how models are productionized).

Who you are:

• Motivated to integrate into a dynamic, high-performance, and exciting start-up environment.
• Passionate about technical excellence and delivering innovative, complete solutions for industry-first products that set new standards in the construction industry.
• Independent, wit