Data Engineer in Test

Who Are We?

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.

What Will You Do?

The Overview

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, BigQuery, 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.

The Specifics

• 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 BigQuery, 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.