Data Migration Specialist

About the Company

Symplast is the leading mobile EHR and Practice Management solution for plastic surgeons and medical spas. Founded in 2013 by board-certified plastic surgeons, we have grown to support more than 3,500 aesthetic professionals nationwide. Our secure, HIPAA-compliant cloud platform delivers an all-in-one mobile experience that streamlines every aspect of practice operations, from front desk management to AI-powered insights, accessible from anywhere in the world. As pioneers in telehealth and telemedicine for aesthetics, we provide unmatched capabilities through our Patient App, advanced Marketing Analytics, robust EHR, Practice Management System, Insurance Billing, and more. With a fully remote and diverse team, we leverage the latest technologies across iOS, Android, macOS, and Windows, all powered by a microservices-based infrastructure on top-tier global cloud providers.

About the Role

The Data Migrations Data Analyst supports Symplast's onboarding process by executing reliable and consistent data migrations using the frameworks and tools developed by our Senior Data Engineering team. This role focuses on the hands-on execution of migration steps, running SQL queries, operating Python-based workflows, validating data consistency, and identifying anomalies prior to ingestion. Reporting to the Data Engineering Manager and working closely with the Data Migrations Project Manager and the Technical Onboarding Manager, this individual ensures that each migration follows established procedures, meets quality standards, and maintains HIPAA-compliant handling of sensitive healthcare data. The ideal candidate has strong analytical skills, is comfortable working with SQL and Python at an intermediate level, and can learn structured technical processes quickly. While this role is primarily internal and not client-facing, clear communication, quality-orientation and documentation skills are important when collaborating with technical stakeholders.

Responsibilities

• Execute end-to-end migration workflows using predefined frameworks in Python and Azure Databricks.
• Run medium-to-advanced SQL queries to detect inconsistencies such as duplicated identifiers, missing fields, invalid formats, or unexpected data patterns.
• Identify data anomalies and escalate findings to the Technical Onboarding Manager or Data Migrations Project Manager.
• Operate existing Python scripts to support ingestion, validation checkpoints, and consistency checks.
• Follow established SOPs to ensure accuracy and reliability across multiple concurrent migrations.
• Collaborate with Data Engineers to communicate anomalies, clarify data structures, or resolve ingestion-related issues.
• Maintain precise documentation of results, counts, mismatch findings, and other technical notes.
• Contribute to internal communication when needed, summarizing technical outputs for non-technical partners (nice to have, not required).
• Maintain strict HIPAA-compliant handling of sensitive patient and clinical data throughout all migration steps.

Qualifications

• 1–3 years of experience in data analysis, data operations, or a technical support role within software or health tech environments.
• Proficiency in SQL (intermediate to advanced), including joins, aggregations, and integrity checks.
• Ability to run, interpret, and adjust Python scripts; intermediate Python skills required to modify existing logic based on client-specific requirements.
• Strong analytical skills, with the ability to identify data inconsistencies quickly and accurately.
• Ability to learn structured technical processes with precision and minimal supervision.
• Familiarity with Azure Databricks, S3, or SFTP environments is a plus (not required).
• Excellent communication skills for internal collaboration with Project Managers and engineers.
• Knowledge of healthcare data or EHR migrations is a strong advantage (Understanding of HIPAA, PHI handling, or experience with healthcare data).
• Comfortable working in a fast-paced environment, managing multiple migration executions in parallel.