Staff Data Engineer - Data Operations

The Role:

This position offers a fast-paced environment with a strong team of diverse engineers that are central in building Arine's data operations infrastructure. You will have the opportunity to have a direct impact on the scalable ingestion and operational architecture that supports our internal data platform. Working alongside our analytics engineers who focus on dbt transformations, you will own the critical "EL" (Extract-Load) infrastructure that feeds our medallion architecture, ensuring raw data flows seamlessly from our platform sources into our staging layer where analytics engineers can build robust dbt models for our data science, machine learning/AI, and reporting teams.

Are You a Good Fit?

The Senior Data Engineer will be responsible for architecting, building, and maintaining scalable data ingestion infrastructure and operational systems that support our medallion architecture (staging → intermediate → marts). This role focuses primarily on the "EL" (Extract-Load) portion of our ELT stack, working closely with analytics engineers who own the dbt transformation layer. You will be responsible for building robust, configuration-driven systems and event-driven processes that scale effectively to handle large enterprise datasets. This position requires expertise in scalable, incremental data migration from sources like RDS and DynamoDB into Snowflake, using tools like Kinesis, Airbyte, or other open-source solutions. You must be comfortable with containerization and building maintainable, configuration-driven toolsets that diverse engineering profiles can utilize effectively.

What You'll be Doing:

• Architecting and implementing scalable data ingestion infrastructure from platform sources (RDS, DynamoDB) into Snowflake
• Building event-driven data pipelines using tools like Kinesis, Airbyte, or other open-source ingestion frameworks that scale effectively
• Designing systems that support our medallion architecture and enable smooth data flow into the staging layer
• Creating configuration-driven, containerized toolsets that can be easily used and maintained by diverse engineering profiles
• Collaborating with analytics engineers to ensure smooth data flow into the staging layer for dbt transformations
• Implementing incremental data migration strategies for large-scale healthcare datasets
• Building monitoring and alerting systems for data ingestion processes and pipeline health
• Applying software engineering best practices including test-driven development and modular design to data infrastructure
• Refactoring and rebuilding existing data ingestion processes to improve scalability and operational efficiency
• Working with containerization technologies (Docker, Kubernetes) to create portable and maintainable data solutions
• Supporting the migration to our staging → intermediate → marts medallion structure
• Mentoring team members on data operations best practices and infrastructure design patterns

Who You Are and What You Bring:

• 6+ years of professional experience in data engineering with focus on large-scale data ingestion and infrastructure
• Strong experience with scalable data ingestion tools such as Kinesis, Airbyte, Kafka, or similar open-source solutions
• Proven experience building event-driven ETL/ELT systems that move large datasets from operational databases (RDS, DynamoDB) to data warehouses (Snowflake)
• Deep understanding of software engineering principles including test-driven development, loose coupling, single responsibility, and modular design
• Experience with containerization technologies (Docker, Kubernetes) and building configuration-driven, maintainable systems
• Understanding of medallion/layered data architecture patterns and experience supporting analytics engineering workflows
• Experience with incremental data processing and change data capture (CDC) methodologies
• Hands-on experience with cloud data infrastructure, particularly AWS services (S3, Kinesis, Lambda, Step Functions, RDS, DynamoDB)
• Proven ability to build tools and systems that can be operated by diverse engineering profiles through configuration rather than code changes
• Experience working with large healthcare datasets and understanding of data privacy and compliance requirements
• Demonstrated ability to refactor and improve existing data infrastructure for better scalability and operational efficiency
• Strong collaboration skills working with analytics engineers, data scientists, and ML engineers
• Excellent verbal and written communication skills with ability to explain technical infrastructure concepts to diverse audiences
• Passion for building robust, maintainable, and operationally excellent data systems

Remote Work Requirements:

• An established private work area that ensures information privacy
• A stable high-speed internet connection for remote work
• This role is remote, but you will be required to come to on-site meetings multiple times per year. This may be in the interview process, onboarding, and team meetings

Perks:

Joining Arine offers you a dynamic role and the opportunity to contribute to the company's growth and shape its future. You'll have unparalleled learning and growth prospects, collaborating closely with experienced Clinicians, Engineers, Software Architects, Data Scientists, and Digital Health Entrepreneurs.

The posted range represents the expected base salary for this position and does not include any other potential components of the compensation package, benefits, and perks. Ultimately, the final pay decision will consider factors such as your experience, job level, location, and other relevant job-related criteria. The base salary range for this position is: $165,000-180,000/year.