Software Engineer - Data Quality

What You Will Do

In this role you will:

Build performant AI-based anomaly detection and monitoring systems, using machine learning and supporting statistical methods to analyze big data at scale.

Design, implement, test, deploy, operate, and tune the online services, automation, and tooling that monitor Truveta's data, contributing hands-on to every stage of the software development lifecycle.

Detect and characterize data-quality issues such as missing or late feeds, volume and distribution shifts, schema and contract drift, duplication, and implausible clinical values.

Develop and evaluate detection approaches, from statistical baselines and time-series methods through machine-learning models, and measure them on precision, recall, and alert usefulness rather than accuracy alone.

Turn detection output into signals people can act on: alerts with root-cause context, data-quality metrics, and dashboards that engineering and clinical teams trust.

Work with data-processing engineers to build quality checks into pipelines so that defects are caught where they are introduced.

Collaborate with cross-functional teams including product managers, clinical informaticists, architects, and other engineers to define what quality means for each data domain.

Take part in design reviews, code reviews, and the on-call rotation for the services you own.

Required Qualifications

Bachelor's degree in Computer Science or a related field, or equivalent practical experience.

1+ of experience building production-quality services on a cloud platform such as Azure, AWS, or GCP.

2+ years writing Python for machine learning, AI-based data analytics, or statistical modeling.

Hands-on experience across the software development lifecycle: design, implementation, testing, deployment, operations, and performance.

Working knowledge of the statistics and model-evaluation methods that underpin that modeling work.

Strong problem-solving skills and the ability to learn a new domain quickly.

Clear written and spoken communication, and comfort collaborating across disciplines.

Preferred Qualifications

Experience developing and evaluating AI or machine-learning approaches for anomaly detection, with supporting knowledge of statistical methods, time-series analysis, or data-quality monitoring.

Experience working as part of distributed teams, particularly across India and the United States.

Experience processing large datasets with Spark, Databricks, or comparable distributed frameworks, and strong SQL.

Experience running services on Microsoft Azure.

Familiarity with data-quality and observability tooling such as Great Expectations, Deequ, Soda, or equivalent in-house frameworks.

Experience operating machine-learning models in production, including drift monitoring and retraining.

Exposure to healthcare, clinical, or other regulated data.