Data Engineer in Test

Prolaio believes that continuous learning and collaboration can make a significant difference in how heart care is administered. We are creating smarter ways to address heart disease and heart risks by uniting patients, care teams, and researchers on a secure, technology-enabled platform that drives clinical innovation and offers a path towards better patient outcomes.

This is precision cardiology, and we know it’s within reach.

As a Data Engineer in Test, you will play a critical role in advancing Prolaio’s mission to transform heart care. In this position, you will collaborate closely with cross-functional teams to validate and optimize data pipelines, ensuring the accuracy and reliability of the data infrastructure that underpins our SaMD products. Your work will be essential in delivering scalable, high-quality data systems that directly support the development of life-changing heart care solutions for patients.

We are looking for a highly driven individual with deep expertise in testing data pipelines, analytical workflows, and large datasets. The ideal candidate will have hands-on experience with cloud-based Data Lake environments (including services like Google Cloud Storage, Big Query, Databricks, or Snowflake), and generating synthetic data to support testing where real patient data is limited, restricted, or unavailable. Your contributions will ensure the integrity, performance, and scalability of our data infrastructure, aligning with our commitment to delivering safe, effective, and accessible heart care solutions.

• Develop and execute comprehensive test plans, test cases, and automated test suites for data pipelines to ensure data quality, consistency, and system performance.
• Test and validate ETL/ELT processes and data workflows across Data Lake environments, confirming the accuracy of data transformations and adherence to business logic.
• Validate data ingestion, processing, and storage within scalable cloud data platforms such as Big Query, ensuring computed data correctly reflects raw time-series biosensor inputs.
• Design, build, and maintain synthetic data generation tools and datasets that mimic real-world time-series biosensor data, enabling thorough test coverage where real patient data is limited or restricted.
• Collaborate with data engineers and data scientists to define testing requirements and ensure data accuracy throughout the entire data lifecycle.
• Conduct performance testing and benchmarking on data systems to evaluate scalability under high data loads and identify performance bottlenecks.
• Monitor and report on key performance metrics, identifying potential data anomalies, inconsistencies, or performance issues.
• Contribute to the continuous improvement of testing processes, automation frameworks, and tools used across data workflows.

• Impactful Work:You will join in the fight against heart failure (HF) and hypertrophic cardiomyopathy (HCM) with the goal of extending and saving the lives of our patients while also being at the forefront of changing the healthcare industry through technology.
• Innovative Environment:You will be part of an organization doing something that’s never been done before.
• Professional Growth:You will join a growing team and have a substantial impact on our daily and future operations with the opportunity to continuously learn and grow.
• Collaborative Team:You will be part of a team of collaborative, curious, and committed individuals focused on the collective good, inclusiveness, scientific excellence, and advancing digital health for cardiology.

• Bachelor's degree in Computer Science, Data Science, or a related field.
• 3+ years of experience in software testing, with a specific focus on data pipelines, Data Lake, and data warehouses.
• Strong understanding of data engineering concepts and technologies, including ETL/ELT processes, Data Lake , and cloud-based data warehouses.
• Proficiency in SQL and Python for data querying, analysis, and test automation.
• Experience working with cloud-based data platforms, particularly Google Cloud Platform (GCP) and Big Query.
• Demonstrated experience generating synthetic data for testing purposes, including designing datasets that realistically represent production data characteristics.
• Familiarity with data testing tools and frameworks such as pytest, unittest, or TestNG.
• Strong knowledge of time-series data processing, analysis techniques, and best practices.
• Excellent problem-solving,…