Senior Data Engineer

Analytica

$115K — $145K *
Healthcare
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.
  • 8+ years of data engineering experience in enterprise environments.
  • 3+ years supporting CMS or federal healthcare programs.
  • Experience with production-grade data platforms in regulated environments.
  • Expert-level experience with Databricks Lakehouse Platform.

Responsibilities

  • Design, develop, and maintain scalable data pipelines using Databricks and cloud services.
  • Build and optimize ETL/ELT workflows for analytics and machine learning initiatives.
  • Develop medallion architecture patterns within Databricks Lakehouse environments.
  • Implement data integration solutions across diverse healthcare data sources.
  • Establish DataOps processes for automated and observable data pipelines.

Benefits

  • Opportunities for career advancement and professional development.
  • Work on large-scale healthcare modernization initiatives.
  • Be part of an innovative team focused on data quality and operational excellence.
  • Collaborate with experts in data architecture and analytics.
  • Access to training related to cloud-native technologies and data governance.
Full Job Description
Analytica is seeking a Senior Data Engineer to support large-scale Health care data modernization and analytics initiatives. This role is responsible for designing, building, optimizing, and operating enterprise-grade data platforms and pipelines that support mission-critical healthcare programs. The ideal candidate possesses deep expertise in cloud-native data engineering, DataOps practices, and regulated healthcare environments.

The Senior Data Engineer will lead the development and maintenance of scalable, secure, and auditable data pipelines while ensuring data quality, reliability, and compliance with federal healthcare regulations and CMS data governance requirements. This individual will serve as a technical leader, collaborating with data architects, data scientists, business analysts, and program stakeholders to deliver high-quality data products and operational capabilities.

Key Responsibilities:

Data Engineering & Platform Development
  • Design, develop, and maintain scalable batch and streaming data pipelines using Databricks and/or cloud-native services.
  • Build and optimize ETL/ELT workflows that support operational reporting, analytics, machine learning, and data-sharing initiatives.
  • Develop and maintain medallion architecture patterns (Bronze, Silver, Gold) within Databricks Lakehouse environments.
  • Implement data integration solutions across multiple structured and unstructured healthcare data sources.
  • Design reusable frameworks, templates, and accelerators that improve engineering productivity and consistency.

Data Operations (DataOps)
  • Establish and maintain DataOps processes that support reliable, automated, and observable data pipelines.
  • Monitor pipeline health, performance, data quality, and operational SLAs.
  • Implement automated testing, deployment, version control, and CI/CD practices for data products.
  • Develop proactive monitoring, alerting, and incident response procedures.
  • Troubleshoot and resolve production data issues while minimizing operational impact.

Healthcare & Regulatory Compliance
  • Ensure solutions comply with CMS, federal, and healthcare-specific security, privacy, and data governance requirements.
  • Support environments containing protected health information (PHI), personally identifiable information (PII), and other sensitive healthcare datasets.
  • Implement auditability, lineage, metadata management, and data quality controls.
  • Partner with governance and security teams to ensure compliance with applicable standards and policies.

Architecture & Technical Leadership
  • Collaborate with solution architects and customer stakeholders to define data platform strategy and implementation roadmaps.
  • Lead technical design reviews and contribute to enterprise data architecture decisions.
  • Optimize data storage, processing performance, and cost management within cloud environments.
  • Mentor junior engineers and promote engineering best practices across teams.

Required Qualifications:

Experience
  • Bachelor's degree in Computer Science, Information Systems, Engineering, or related technical discipline.
  • 8+ years of data engineering experience, including large-scale enterprise data environments.
  • 3+ years supporting CMS or other federal healthcare programs.
  • Experience operating and supporting production-grade data platforms in regulated environments.
  • Demonstrated experience with DataOps and modern data engineering practices.

Technical Skills
  • Expert-level experience with Databricks Lakehouse Platform.
  • Strong proficiency with Apache Spark, PySpark, SQL, and Python.
  • Experience designing and maintaining operational data pipelines at enterprise scale.
  • Hands-on experience with cloud data platforms in AWS or Azure cloud environments.
  • Experience implementing CI/CD pipelines using Git-based development workflows.
  • Expertise in data quality, observability, lineage, metadata management, and monitoring frameworks.
  • Experience with orchestration and workflow management tools.
  • Strong knowledge of lakehouse, warehouse, and modern data architecture patterns.

Regulatory & Healthcare Knowledge
  • Experience supporting CMS, Medicare, Medicaid, or other federal healthcare data programs.
  • Understanding of healthcare data governance, security, privacy, and compliance requirements.
  • Experience working with healthcare data standards and regulated datasets.
  • Familiarity with Federal Information Security requirements and operational controls.

Preferred Qualifications
  • Databricks Certified Data Engineer Professional
  • Additional Databricks certifications.
  • Experience supporting cloud-native analytics platforms for CMS.
  • Experience implementing Delta Lake, Unity Catalog, and advanced Databricks governance capabilities.
  • Familiarity with machine learning operations (MLOps) and AI/ML data pipelines.
  • Experience with Collibra, Informatica, Unity Catalog or comparable data governance platforms.
  • Experience supporting FedRAMP or FISMA-compliant environments.
  • Prior experience leading technical teams or serving as a technical lead.

Desired Competencies:
  • Deep technical expertise in modern data engineering and DataOps practices.
  • Strong problem-solving and troubleshooting abilities.
  • Exceptional written and verbal communication skills.
  • Ability to translate business requirements into scalable technical solutions.
  • Proven ability to operate effectively in highly regulated healthcare environments.
  • Commitment to data quality, operational excellence, and continuous improvement.

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