Healthcare Data Engineer – AI/ML Enablement

Job Description – Healthcare Data Engineer – AI/ML Enablement

Location: Remote

Type: Contract

Accelyst is an innovative AI Consultancy that leverages a unique catalog of industry specific Agents and leading-edge AI platforms to deliver tangible, integrated, secure and ROI-optimized solutions. We combine deep industry and technical expertise to enable rapid deployment of innovative AI-driven capabilities to augment and automate client workflows for employees, customers, prospects, and investors.

Why Accelyst?

Join Accelyst to be part of a dynamic team that leverages AI-driven technology to make a positive impact. Our leadership, with Big Four Consulting experience, fosters a nimble, client-focused environment, minimizing bureaucracy to enhance delivery and professional growth. You'll work on complex AI projects that challenge and inspire, meeting high client expectations. Additionally, we benefit from our profit-sharing model, reflecting our commitment to respect and integrity for all employees.

Role Overview

We are seeking a Healthcare Data Engineer to support AI/ML initiatives through advanced data preparation and management. This role involves cleaning, rationalizing, normalizing, and integrating large healthcare datasets (claims, member, provider, clinical data) to ensure readiness for AI-driven analytics and automation. You will collaborate with data scientists, engineers, and AI teams to deliver high-quality, compliant data pipelines for predictive analytics, risk stratification, fraud detection, and other use cases.

In this role, you will be responsible for designing, developing, testing, and integrating data solutions to support automated workflows, bots and AI Agents that span legacy platforms, such as:

• Claims processing systems
• Member management platforms
• Provider network management systems
• Care management platforms

Responsibilities

• Perform data cleaning : identify and resolve errors, inconsistencies, duplicates, and missing values.
• Conduct data rationalization and normalization to align with healthcare data standards and structures (ICD-10, CPT, SNOMED).
• Aggregate and consolidate datasets from multiple systems (EHRs, claims, third-party vendors).
• Execute data extraction workflows to generate AI-ready features.
• Conduct data quality assurance and validation checks to ensure reliability and accuracy.
• Collaborate with data scientists and AI/ML teams to align data preparation with project goals.
• Implement automation and scripting for efficient data handling.
• Maintain thorough documentation of data processes, transformations, and metadata.
• Ensure compliance with HIPAA, data governance, and security standards .

Qualifications

• Strong experience in healthcare data engineering (claims, EHR, clinical datasets).
• Proficiency in data cleaning, normalization, aggregation, and feature extraction .
• Knowledge of healthcare standards and coding systems (ICD-10, CPT, SNOMED, HL7, FHIR).
• Proficiency with SQL, Python, and automation frameworks for data workflows.
• Hands-on experience integrating data from multiple healthcare systems and vendors .
• Knowledge of:
• Cloud-Based Solutions, for example: Amazon Web Services (AWS) with its healthcare-specific services, Microsoft Azure's healthcare cloud, and Google Cloud Healthcare
• APIs that provide scalable infrastructure for data storage, processing, and compliance with healthcare regulations, like HIPAA.
• Specialized Payer Platforms, such as Apixio, Veracyte and Health Catalyst.
• Enterprise Integration Platforms like Mulesoft, Informatica, and Talend.
• Excellent collaboration skills and ability to partner with AI/ML teams.
• Strong understanding of data governance, HIPAA compliance, and audit documentation .