Staff Data Platform Engineer - ML Data Foundations (Remote)

Omada Health, Inc.; United States; Remote; USD 193,000 - 253,000; Full time

Benefits offered by this job

Remote-first culture

Generous annual cash bonus

Health, dental, and vision insurance

401k retirement savings plan

Equity grants

Employee stock purchase plan (ESPP)

Job summary

Omada Health is seeking a Staff Software Engineer, Data Engineering to lead the design and development of a production data platform powering machine learning across the organization.

You will collaborate with Data Scientists, Applied AI Engineers, and Product Engineers to create reusable feature pipelines and data foundations that support model training, experimentation, and production inference. The role emphasizes scalable, governed data products and end-to-end data quality.

Qualifications

• 8+ years building large-scale production data platforms and distributed data pipelines.
• Experience designing reusable datasets powering machine learning, experimentation, or advanced analytics.
• Demonstrated partnership with Data Scientists to productionize feature engineering workflows.
• Experience leading cross-team technical initiatives and influencing engineering direction.
• Strong experience with cloud-native data platforms such as AWS.
• Experience building production data systems using Databricks, Iceberg, Spark, Redshift, Snowflake, or similar technologies.
• Developing reliable batch and streaming data pipelines.

Responsibilities

• Design, build, and maintain reusable feature datasets for ML use cases including personalization, engagement, risk prediction, churn modeling, and experimentation.
• Establish self-service foundations that democratize dataset creation across the data organization.
• Partner with Data Scientists to translate modeling requirements into production-ready feature pipelines across the full model lifecycle.
• Identify source data, transformations, and historical windows for feature engineering; build shared feature definitions across models.
• Balance freshness, correctness, latency, and cost in data pipelines.
• Build datasets supporting historical training and future production inference.
• Design batch and streaming pipelines transforming healthcare, behavioral, product, and operational data into ML-ready datasets.
• Build reliable data processing systems using Python, SQL, Spark, and cloud platforms.
• Optimize large-scale distributed processing for performance and cost.
• Create modular, testable, observable data pipelines that evolve with product requirements.
• Ensure data quality via testing, anomaly detection, schema validation, and monitoring.
• Collaborate with platform teams for near real-time feature generation where appropriate.
• Improve reproducibility by standardizing feature computation across experimentation and production.
• Support rapid experimentation without sacrificing maintainability.
• Establish engineering standards for correctness, documentation, and maintainability.]
• Lead architecture and design of large-scale ML data systems, driving reusable patterns and platform capabilities across Data Engineering.
• Influence technical direction across multiple teams, aligning with stakeholders during early design phases.
• Translate business requirements into technical specs, ensuring consistent business logic across systems.
• Mentor engineers on distributed data processing and scalable data modeling.

Skills

Advanced SQL

Python/Java/Scala

Apache Spark

Airflow

Data modeling

Cloud AWS

Distributed data systems

Education

Bachelor’s degree in Computer Science or related field

Tools

Databricks

Iceberg

Redshift

Snowflake

Spark

Kafka

Docker

Kubernetes

Airflow

S3

Lambda

Tableau

Datadog

OpenMetadata

GitLabCI

Job description

Omada Health is seeking a Staff Software Engineer, Data Engineering to lead the design and development of a production data platform powering machine learning across the organization.

You will collaborate with Data Scientists, Applied AI Engineers, and Product Engineers to create reusable feature pipelines and data foundations that support model training, experimentation, and production inference. The role emphasizes scalable, governed data products and end-to-end data quality.