Staff Software Engineer, Data Products

Omada Health

1d•$176,000 - $253,000•Remote

About The Position

Omada Health is seeking a Staff Software Engineer, Data Engineering to lead the design and development of the production data platform that powers machine learning across Omada. In this role, you will partner closely with Data Scientists, Applied AI Engineers, Product Engineers, and fellow Data Engineers to identify, design, and build trusted, reusable datasets foundations that serve as the foundation for feature engineering, model training, experimentation, and production inference. Rather than building one-off pipelines for individual models, you'll create scalable data products and feature pipelines that enable multiple machine learning use cases while ensuring consistency, reliability, and governance across the ML lifecycle. You will own the technical design of feature datasets—from ingesting raw behavioral, clinical, and operational data through transforming, validating, and publishing production-grade datasets that are reusable across modeling teams. This role is ideal for someone who enjoys solving complex data problems, designing scalable distributed data systems, and enabling machine learning through well-engineered data foundations.

Requirements

8+ years building large-scale production data platforms and distributed data pipelines.
Experience designing reusable datasets that power machine learning, experimentation, or advanced analytics.
Demonstrated experience partnering closely with Data Scientists to productionize feature engineering workflows.
Experience leading cross-team technical initiatives and influencing engineering direction.
Strong experience working with cloud-native data platforms such as AWS.
Experience building production data systems using Databricks, Iceberg, Spark, Redshift, Snowflake, or similar technologies.
Experience developing reliable batch and streaming data pipelines.
Expert SQL with strong data modeling skills.
Strong programming skills in Python, Java, or Scala.
Experience with Apache Spark or similar distributed compute frameworks.
Experience with Airflow or similar orchestration platforms.
Experience designing dimensional models, event models, and feature datasets.
Experience implementing testing, CI/CD, observability, and production monitoring for data pipelines.
Understanding of Feature Stores and ML data lifecycle concepts.
Understanding of software engineering best practices, distributed systems, and cloud-native architectures.
An exceptional people leader who develops engineers into future technical leaders.
Comfortable influencing senior executives and cross-functional partners.
Skilled at balancing business priorities with long-term technical investments.
Able to communicate complex technical concepts to both technical and non-technical audiences.
Passionate about building trusted data platforms that enable the business.
Bachelor’s degree in Computer Science or a similar discipline preferred.

Nice To Haves

Experience working with healthcare, behavioral, or other large-scale event data is a plus.
Experience with Lakehouse Architecture such as Databricks, Iceberg is a strong plus.
Experience with streaming technologies such as Kafka, Flink, or Spark Structured Streaming.
Familiarity with NoSQL Databases (document & graph databases Nepture, Neo4j etc.)
Familiarity with feature stores or feature management platforms.
Familiarity with model training pipelines and MLOps workflows.
Experience supporting personalization, recommendation, ranking, or predictive modeling systems.
Familiarity with model training pipelines and MLOps workflows.
Experience designing data platforms for experimentation.
Healthcare industry experience is a plus.
Experience with Data/AI Governance.

Responsibilities

Design, build, and maintain reusable feature datasets that support machine learning use cases including personalization, engagement, risk prediction, churn modeling, recommendation systems, and experimentation.
Establish self-service foundations that streamline and democratize dataset creation across the data organization.
Partner with Data Scientists to translate modeling requirements into production-ready feature pipelines, supporting the full model lifecycle from exploration to deployment.
Identify source data, transformations, and historical windows needed for feature engineering. Help define and build shared, reusable feature definitions across models rather than one-off datasets.
Balance features freshness, correctness, latency, and computational efficiency when designing data pipelines.
Build datasets that support both historical model training and future production inference.
Design and implement batch and streaming pipelines that transform raw healthcare, behavioral, product, and operational data into trusted ML-ready datasets.
Build reliable data processing systems using Python, SQL, Spark, and modern cloud data platforms.
Optimize large-scale distributed processing for performance, scalability, and cost.
Design data pipelines that are modular, testable, observable, and easy to evolve as product requirements change.
Ensure data quality through testing, anomaly detection, schema validation, and pipeline monitoring.
Partner with platform teams to support near real-time feature generation where appropriate.
Improve reproducibility by standardizing feature computation across experimentation and production.
Support rapid experimentation without sacrificing long-term maintainability.
Ensure data quality through testing, anomaly detection, schema validation, and pipeline monitoring.
Establish engineering standards for correctness, documentation, and maintainability.
Lead architecture and design discussions for large-scale ML data systems, driving adoption of reusable patterns and platform capabilities across Data Engineering.
Influence technical direction across multiple engineering teams, embedding with Product, Engineering, and business stakeholders (Clinical, Finance, Growth, Enrollment) during early design phases to shape data capture requirements at the source.
Translate ambiguous business requirements from Business domain SMEs into concrete technical specs, maintaining consistency of business logic and definitions across systems.
Mentor engineers on distributed data processing, software engineering best practices, and scalable data modeling.