Data Operations Engineer

Sword Health is shifting healthcare from human-first to AI-first through its AI Care platform, making world-class healthcare available anytime, anywhere, while significantly reducing costs for payers, self-insured employers, national health systems, and other healthcare organizations. Sword began by reinventing pain care with AI at its core, and has since expanded into women’s health, movement health, and more recently mental health. Since 2020, more than 700,000 members across three continents have completed 10 million AI sessions, helping Sword's 1,000+ enterprise clients avoid over $1 billion in unnecessary healthcare costs.

Backed by 42 clinical studies and over 44 patents, Sword Health has raised more than $500 million from leading investors, including Khosla Ventures, General Catalyst, Transformation Capital, and Founders Fund. Learn more at

Sword Health is looking for an exceptional candidate to join our Growth team as a Data Operations Engineer. This individual will support the integration of clients (self insured employers and health plans) into Sword’s solutions by preparing, validating and maintaining data pipelines that can process large volumes of healthcare data. This role is ideal for a motivated, detail-oriented candidate who is eager to learn and ensure that information flows accurately and on time.

The mission of our Data Operations team is to deliver high-quality, accurate data that supports Sword’s products and clients. We focus on maintaining strong data quality standards, improving operational processes, and ensuring smooth data integrations across the organization.

• ETL (Extract, Transform, Load) Pipelines:
  Ensure the reliable operation of existing ETL pipelines by monitoring data flows, validating and reconciling data, identifying and resolving issues, and maintaining high standards of data quality across multiple healthcare data sources;
• Data Validation & Quality Management:
  Develop and maintain automated validation and cleansing rules to identify and resolve data quality issues such as missing data, mismatched fields, and invalid clinical or billing codes (e.g., ICD, CPT);
• Monitoring, Alerting & Incident Response:
  Configure and manage monitoring and alerting mechanisms to detect processing failures, latency issues, and data anomalies in near real time, and drive timely resolution;
• Issue Investigation & Root Cause Analysis:
  Lead root-cause analysis for data and pipeline issues, partnering with data engineering and upstream data providers to resolve defects and prevent recurrence;
• Cross-Functional Collaboration & Process Improvement:
  Work closely with data engineering, product, customer success, and operations teams to continuously improve data reliability, operational workflows, and documentation

• Healthcare Data Expertise:
  Strong working knowledge of eligibility, medical claims, pharmacy claims, and prior authorization data, including how these datasets are structured, interpreted, and used across the healthcare ecosystem;
• Data Governance & Compliance:
  Experience handling Protected Health Information (PHI) in accordance with HIPAA and other applicable regulations, with a solid understanding of data governance, privacy, and security best practices;
• Data Querying & Analysis:
  Proven experience writing and optimizing SQL queries to extract, reconcile, and analyze large healthcare datasets in support of operational and data quality needs;
• Data Operations & Integration Knowledge:
  Working understanding of ETL processes, data integration workflows, secure data transport (e.g., SFTP), and data encryption, with a focus on operating and supporting existing data pipelines;
• Cross-Functional & External
  
  Collaboration:
  
  Comfortable partnering with data engineering, product, customer success, clinical operations, and external vendors or payers to validate requirements, clarify data definitions, support SLAs, and ensure timely, accurate data delivery;
• Operational Rigor & Problem Solving:
  Detail-oriented and process-driven, with a strong ability to diagnose issues, reconcile data discrepancies, document workflows, and propose process improvements that reduce…