Specialist Data Engineer

ABOUT THIS POSITION

Waystar, the leading Healthcare Revenue Cycle Management (RCM) technology company, is seeking an expert-level Specialist Data Engineer to join our Data Architecture team. In this advanced role, you will own the design, optimization, and governance of complex data pipelines and infrastructure that power data-driven products and analytics used by clients and internal stakeholders. You’ll serve as a subject matter expert (SME) in cloud data architecture, data product engineering, and performance tuning, while ensuring solutions align with enterprise data strategy. You will work with highly complex healthcare datasets across multiple platforms (Snowflake, BigQuery, SQL Server, Postgres) and contribute to the architectural evolution of Waystar’s data ecosystem. This role is ideal for a senior engineer with specialist-level depth in data modeling, performance engineering, ML Ops, and cloud data platform optimization.

The ideal candidate will have a deep understanding of healthcare data and RCM processes, with strong technical expertise and the ability to collaborate across data, product, and business teams. You will also play a key role in implementing ML Ops practices to operationalize machine learning models and drive continuous improvement. As a senior member of the team, you will provide mentorship to junior engineers, set best practices, and demonstrate thought leadership by influencing technical decisions and guiding the evolution of our data infrastructure to support future growth

WHAT YOU'LL DO

1. Data Architecture and Engineering

· Design and implement scalable, high-performance data pipelines using Snowflake and Google BigQuery to enable actionable insights.

· Develop and maintain ETL/ELT processes to ingest, transform, and store structured and unstructured data, ensuring data accuracy and timeliness.

· Optimize data pipelines for performance, scalability, and cost efficiency, driving faster insights and customer value.

· Build data infrastructure to support real-time and batch processing use cases.

· Establish and enforce data quality standards and monitoring to ensure consistency and accuracy.

· Monitor and troubleshoot data pipeline performance issues and implement proactive improvements.

· Recognized SME in at least one area of cloud-based data engineering (e.g., BigQuery performance optimization, advanced Snowflake design patterns, ML Ops infrastructure).

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2. Data Modeling and Integration

· Design and implement data models to support healthcare revenue cycle workflows and customer reporting requirements.

· Integrate data from multiple source systems, including SQL Server, Postgres, and external APIs, ensuring data consistency and reliability.

· Create and maintain data dictionaries and metadata to support self-service analytics and business intelligence.

· Implement data partitioning, indexing, and clustering strategies to improve query performance.

3. ML Ops and Machine Learning Pipeline Support

· Collaborate with Data Scientists to operationalize machine learning models and ensure smooth deployment and monitoring in production.

· Build and maintain feature stores and data sets used for machine learning models.

· Implement ML Ops best practices, including model versioning, retraining, and monitoring for drift and performance degradation.

· Ensure that ML models are delivering measurable business outcomes and value to customers.

4. Value Delivery and Customer Outcome Achievement

· Partner with business and product teams to understand customer goals and translate them into data engineering solutions.

· Ensure that data products and pipelines deliver measurable business value and directly improve customer outcomes (e.g., faster claim processing, improved payment accuracy).

· Establish KPIs to track the business impact of data solutions and continuously improve them.

· Actively identify opportunities to improve data quality and speed of delivery to enhance customer experience.

7. Automation and Performance Optimization

· Automate repetitive data processing tasks using Python and SQL to reduce manual effort and increase efficiency.

· Monitor and optimize data pipeline performance, addressing bottlenecks and failures.

· Leverage Snowflake’s performance features (e.g., micro-partitions, clustering, materialized views) to optimize query performance.

· Implement data orchestration using tools like Airflow or dbt to automate and monitor workflows.

WHAT YOU'LL NEED

Required:

· Bachelor's or Master’s degree in Computer Science, Data Engineering, or comparable experience.

· 8-10 years of experience in data engineering with emphasis on cloud platforms.

· Experience working with relational databases such as SQL Server a