Senior Software Engineer, Data Engineering Remote

Senior Software Engineer, Data Engineering

Remote, USA

Omada Health is on a mission to inspire and engage people in lifelong health, one step at a time.

We are dedicated to leveraging data to drive strategic decision-making and operational efficiency. Our team is passionate about harnessing the power of data to solve complex problems and deliver impactful insights.

• Data Architecture:
  Design, develop, and implement scalable, secure, and efficient data solutions that meet the needs of the organization.
• Data Modeling:
  Create and maintain logical and physical data models to support business intelligence, analytics, and reporting requirements.
• Pipeline Engineering:
  Design, build, and optimize ETL (Extract, Transform, Load) processes and data pipelines to ensure smooth and efficient data flow from various sources.
• Data Integration:
  Integrate diverse data sources, including APIs, databases, and third‑party data, into a unified data platform.
• Performance Optimization:
  Monitor and optimize the performance of data systems and pipelines to ensure low latency and high throughput.
• Data Quality and Governance:
  Implement data quality checks, validation processes, and governance frameworks to ensure the accuracy and reliability of data.
• Collaboration:
  
  Partner closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet their needs.
• Documentation:
  Maintain comprehensive documentation of data architectures, models, and pipelines for ongoing maintenance and knowledge sharing.
• Training:
  Train and collaborate with teammates effectively in data engineering best practices.
• Technical Influence/Leadership:
  Recommend policy changes and establish department-wide procedures. Use extensive experience and knowledge to resolve complex problems.
• Production Management:
  Monitor and manage the production environment to deliver data within defined SLAs.

• Evaluate, benchmark, and improve the scalability, robustness, and performance of our data platform and applications.
• Make significant contributions to the architecture and design of our data processing platform.
• Implement scalable, fault-tolerant, and accurate ETL frameworks.
• Gather and process raw data at scale from diverse sources.
• Collaborate with product management, data scientists, analysts, and other engineers on technical vision, design, and planning.
• Implement and maintain a high level of data quality monitoring in our analytics & ML ecosystem.
• Train and collaborate with teammates effectively in data engineering best practices.
• Lead, document, and collaborate across teams for technical projects.

• You are passionate about building data-driven systems to enable Data Scientists, Data Analysts, and AI/ML Engineers.
• You want to make a difference to empower digital healthcare through data-driven decision making.
• You would like to learn how to build scalable, performant, and reliable data pipelines.

• 5+ years of experience building, maintaining, and orchestrating scalable data pipelines.
• 3+ years of experience as a data engineer developing or maintaining integration with software such as Airflow or any Python-based data pipeline codebase.
• Experience applying a variety of integration patterns for different use cases.
• Experience in backend software development to contribute to distributed computing development and data technologies, with broad experience across systems, contexts, and ideas.
• Experience implementing data pipelines and improving the performance of ETL processes and related SQL queries.
• Experience in data modeling for OLTP and OLAP applications.
• Experience with cloud platforms such as Amazon AWS.
• Familiarity with workflow management tools (Airflow preferred).
• Familiarity with cloud-based data warehouses (Amazon Redshift preferred).
• Exceptional problem-solving and analytical skills.
• Experience working with sensitive data (PHI / PII) and security best practices.
• Familiarity with data governance practices and principles.

• Proficiency in SQL and experience with relational databases (e.g., MySQL, Postgre
  
  SQL).
• Proficiency in Analytical SQL (e.g., analytics…