Hybrid Senior Data Engineer – Data Operations

at Arine

• Job title: Senior Data Engineer – Data Operations
• Job type: Full Time
• Experience level: Senior
• Salary: $150,000 - $170,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Cloud, Docker, DynamoDB, Kafka, Kubernetes, Python, SQL
• Location requirements: Hybrid, United States

About the role

• Senior Data Engineer responsible for building and maintaining scalable data ingestion systems for healthcare data. Optimizing data pipelines and collaborating with analytics engineers in a fast-growing environment.

Responsibilities

• The Senior Data Engineer is responsible for building and maintaining scalable data ingestion infrastructure and operational systems.
• Develop and optimize scalable data ingestion pipelines from platform sources (RDS, DynamoDB) into Snowflake.
• Building event-driven pipelines using Kinesis, Airbyte, or other open-source frameworks to handle high-volume healthcare data.
• Implementing and maintaining a staging-layer architecture that supports the broader medallion (staging → intermediate → marts) structure.
• Creating configuration-driven, containerized toolsets (Docker/Kubernetes) to ensure data solutions are portable and maintainable.
• Ensuring data reliability by building comprehensive monitoring, alerting, and automated testing for all ingestion processes.
• Collaborating with analytics engineers to streamline the flow of data for dbt transformation.
• Applying software engineering best practices, including modular design and test-driven development, to all data infrastructure.
• Refactoring existing ingestion processes to improve performance, cost-efficiency, and scalability.
• Mentoring mid-level and junior engineers through code reviews and sharing best practices in data operations.

Requirements

• 4-6+ years of professional experience in data engineering with a focus on data ingestion and infrastructure.
• Proficiency in Python and SQL, with a track record of building production-grade data pipelines.
• Strong experience with ingestion tools such as Kinesis, Airbyte, Kafka, or similar frameworks.
• Hands-on experience with Snowflake and moving data from operational databases (RDS, DynamoDB) to cloud data warehouses.
• Solid understanding of AWS services (S3, Lambda, Step Functions, RDS).
• Experience with containerization (Docker) and deploying maintainable systems.
• Knowledge of ELT patterns, specifically supporting analytics engineering workflows and dbt.
• Experience with CDC (Change Data Capture) and incremental processing methodologies.
• Detail-oriented mindset regarding data privacy and compliance (HIPAA experience is a plus).
• Strong communication skills, with the ability to collaborate effectively across data science and engineering teams.

Benefits

• Outstanding Team and Culture
• Making a Proven Difference in Healthcare
• Market Opportunity
• Dramatic Growth