Sr. Data Engineer

eHealth

3d•$115,000 - $143,800

About The Position

eHealth is seeking an experienced Senior Data Engineer to help make health insurance more accessible, affordable, and easier to navigate. The role leads the design of scalable, high-performance data pipelines and data solutions, and supports analytics and machine learning initiatives across the business. The engineer works closely with data scientists, software engineers, business stakeholders, data architects, and infrastructure teams to ensure data is reliable, accessible, secure, and actionable in a fast-paced, regulated industry. The position serves as a subject-matter expert for eHealth’s data ecosystem and helps guide architectural decisions, engineering standards, and technology adoption across teams. It also includes mentoring other engineers and promoting effective, responsible use of AI-assisted development tools. Candidates must have a bachelor’s or master’s degree in a relevant technical field and meet the stated data engineering experience requirement, or have an equivalent combination of education and relevant experience. The position offers a base pay range of $115,000–$143,800, with actual pay dependent on factors such as skills, experience, performance, qualifications, department budget, and work location. The total rewards package also includes an annual performance bonus and benefits.

Requirements

Bachelor’s or master’s degree in Computer Science, Engineering, or a related technical field.
At least 5 years of related data engineering experience with a bachelor’s degree, or at least 3 years with a master’s degree, or an equivalent combination of education and relevant experience.
Expert-level SQL for complex query development, optimization, and performance tuning across large datasets.
Strong programming skills in Python or Scala and deep experience with software engineering practices, including CI/CD, git workflows, testing, and code review.
Proven experience architecting solutions on a cloud-native data platform such as Snowflake, BigQuery, Redshift, or Databricks.
Deep working knowledge of modern ETL/ELT frameworks such as dbt, Spark, Informatica, or Matillion, including framework selection and design trade-offs.
Experience with NoSQL databases such as MongoDB, Cassandra, or Hive.
Substantial experience with cloud platforms, preferably AWS, including services such as S3, Glue, Lambda, Redshift, or EMR.
Strong command of data modeling, including star and snowflake schemas, data governance, and security principles, with experience setting standards for others.
Demonstrated experience mentoring engineers and/or leading technical initiatives.
Excellent communication skills and the ability to influence cross-functional stakeholders and technical direction.
Advanced expertise designing scalable data pipelines, ETL/ELT workflows, data models, and real-time streaming solutions using modern data engineering frameworks and cloud-native platforms.
Strong understanding of cloud-based data ecosystems, data quality, observability, lineage, security, and governance principles, and ability to establish scalable engineering standards and best practices.
Ability to analyze complex or ambiguous technical and business problems, evaluate design trade-offs and emerging technologies, and develop practical, scalable solutions.
Ability to explain complex technical concepts to technical and non-technical audiences, build cross-functional partnerships, and influence technical decisions.
Ability to lead technical initiatives, provide technical guidance and code review, mentor engineers, share knowledge, and improve team engineering practices.

Nice To Haves

Hands-on experience with Databricks and Delta Lake.
Deep knowledge of event-driven architectures and tools such as Kafka or Kinesis.
Experience designing RESTful APIs for data delivery and ML model serving.
Experience with containerization and orchestration, including Docker and Kubernetes, in production.
Visualization experience with Tableau, Power BI, or Looker.
Exposure to healthcare or health technology, including EHR, claims data, or call center analytics.
Experience operating in regulated environments such as HIPAA or SOC 2.
Track record of driving automation, data observability, and proactive monitoring initiatives.

Responsibilities

Serve as a subject-matter expert on the data ecosystem, including internal systems and third-party data sources, and guide architectural decisions across teams.
Architect, build, and maintain scalable data pipelines and real-time streaming architectures using frameworks such as Spark, Kafka, and dbt.
Design and drive adoption of workflow automation and orchestration standards using tools such as Apache Airflow or Matillion.
Lead technical design for production-grade ML pipelines with data scientists and ML engineers, including APIs that serve model predictions.
Use AI-assisted development tools to accelerate pipeline development, code review, and testing, and help establish team norms for effective, responsible use.
Own data quality, observability, lineage, and governance strategy, including monitoring, alerting, and metadata-tracking best practices.
Drive logical and physical data modeling with data architects, including scalable schema design decisions.
Partner with DevOps and infrastructure teams on platform architecture, performance optimization, and security and compliance strategy.
Mentor junior and mid-level data engineers through code review, technical guidance, and knowledge-sharing.
Evaluate emerging data tools and technologies and make build-versus-buy and adoption recommendations to engineering leadership.
Demonstrate eHealth’s values in behaviors, practices, and decisions.