Senior Software Engineer, Data Engineering

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

• →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: You'll train and collaborate with teammates effectively in data engineering best practices
• →Technical Influence/Leadership: Recommends policy changes and establishes department-wide procedures. Uses extensive experience and knowledge to resolve complex problems. 
• →Monitor and manage production environment to deliver data within defined SLAs 

• You’ll evaluate, benchmark, and improve the scalability, robustness, and performance of our data platform and applications
• You'll make significant contributions to the architecture and design of our data processing platform
• You’ll implement scalable, fault tolerant, and accurate ETLs frameworks.
• You’ll gather and process raw data at scale from diverse sources
• You’ll collaborate with product management, data scientists, analysts, and other engineers on technical vision, design, and planning
• You’ll implement and maintain a high level of data quality monitoring in our analytics & ML ecosystem
• You’ll train and collaborate with teammates effectively in data engineering best practices
• You will be responsible for leading, documenting, and collaborating across teams for technical projects.

• You are passionate about building data-driven systems to enable Data Scientist, 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 i.e. PHI / PII & security best practices
• Familiarity with data governance practices and principles.

• Proficiency in SQL and experience with relational databases (e.g., MySQL, PostgreSQL).
• Proficiency in Analytical SQL (e.g. Analytics Queries, Distributed database queries) and experience working with massive parallel processing (MPP) databases (e.g., Redshift, BigQuery, Snowflake).
• Proficiency in programming languages such as Python, Java, or Scala.
• Knowledge of data modeling techniques (3NF)  and tools (e.g., ER/Studio, ERwin).
• Software Engineering Mindset: Apply best practices to write elegant, maintainable code and understand automated testing concepts.
• Familiarity with business intelligence tools and environments.
• Familiarity with big data technologies (e.g., Lambda, Hadoop, Spark)
• Software engineering mindset and an ability to write elegant, maintainable code while following engineering best practices 

• Experience with big data technologies such as Hadoop, Spark, or Kafka.

• Experience building data infrastructure, frameworks and automation is a big plus. 
• Understanding automated testing concepts and ability to consistently apply those concepts