Senior Analytics Engineer

HarmonyCares • UNAVAILABLE, UNAVAILABLE

2h

About The Position

The Senior Analytics Engineer transforms complex healthcare business needs into scalable, governed, analytics-ready data solutions. Working closely with clinical, operational, and business stakeholders, this role develops trusted datasets, semantic models, and data products that support reporting, advanced analytics, and organizational decision-making. The position focuses on sustainable data foundations, data quality, productized insights, and self-service analytics.

The role designs and optimizes data transformation and feature-engineering pipelines, reusable data marts, feature stores, semantic models, curated datasets, and dashboards. It supports BI and AI/ML workloads, including preparing consistent model-ready data and helping integrate AI/ML outputs into clinical and operational workflows. Work includes interpreting claims, clinical, and operational healthcare data and applying standards such as ICD-10, CPT, and NDC to support interoperability and consistent analytics. The employee may also perform other job-related assignments as requested.

Requirements

• Bachelor’s degree in information technology, computer science, or a related field, or equivalent experience.
• At least 5 years of experience working with healthcare data in analytics engineering, data engineering, or related data-focused roles, with increasing ownership of data modeling and analytics solutions.
• Strong experience with claims and clinical datasets in payer and/or provider environments, and the ability to translate data into actionable insights.
• Experience designing and building scalable data models, curated data marts, and semantic layers for BI and analytics.
• Deep understanding of healthcare data standards and vocabularies, such as ICD-10, CPT, and SNOMED, and their use in analytics and interoperability.
• Experience with cloud data platforms; Azure is preferred, while AWS or GCP is acceptable. Experience should include modern data-stack components such as data lakes or lakehouses, distributed processing such as Databricks or Spark, and orchestration tools.
• Strong SQL proficiency and experience with at least one programming language such as Python or Scala, focused on data transformation, validation, and performance optimization.
• Familiarity with feature engineering and preparing ML-ready datasets, including assembling training datasets and supporting predictive-use-case data pipelines.
• Ability to translate ambiguous business problems into structured, scalable data solutions rather than one-off builds.
• Experience in a healthcare services, payer, or provider organization.
• Strong communication skills and ability to explain technical concepts in business-friendly terms and influence technical and non-technical stakeholders.

Nice To Haves

• Hands-on experience with Databricks, including Spark, notebooks, Delta Lake, and workflows.
• Knowledge of CI/CD, infrastructure-as-code, or data-platform automation.
• Prior experience with healthcare integration engines such as Rhapsody or Mirth.
• Prior experience with data governance, HIPAA compliance, and PHI handling.

Responsibilities

• Collaborate with clinical, operational, and business teams to understand workflows and translate requirements into scalable data models, curated datasets, standardized metrics, and AI-ready data assets.
• Break down complex data and AI requirements, identify key assumptions, and design maintainable, production-grade data and feature pipelines.
• Design, build, and optimize data transformation pipelines and analytics models using cloud platforms such as Azure Data Factory, Databricks, or Microsoft Fabric.
• Develop and maintain feature-engineering pipelines for machine-learning use cases such as risk scoring, readmission prediction, and utilization forecasting.
• Build reusable data marts and feature stores for BI and AI/ML workloads.
• Partner with data scientists and ML engineers to prepare high-quality, model-ready datasets and maintain consistency between training and inference data.
• Help integrate AI/ML outputs into clinical and operational workflows, including alerts, prioritization queues, and decision-support tools.
• Develop and optimize semantic models, curated datasets, and dashboards in Databricks, Power BI, or Tableau, aligning them with standardized metrics and ML-derived insights.
• Work with claims, clinical, and operational healthcare datasets, ensuring they are correctly interpreted for analytics and machine-learning use cases.
• Ingest, transform, and normalize healthcare data using standards such as ICD-10, CPT, and NDC to support interoperability and consistency.
• Perform other job-related assignments as requested.

Benefits

• Health, dental, vision, disability, and life insurance
• 401(k) retirement plan with company match
• Tuition, professional license, and certification reimbursement
• Paid time off, holidays, and volunteer time
• Paid orientation and training

What This Job Offers

• Job Type: Full-time
• Career Level: Senior