Health Data Engineering Lead

Guidehouse•Tysons Corner, VA

About The Position

Lead technical delivery for complex healthcare data and analytics initiatives, providing day-to-day direction to data engineers, analysts, and other technical team members. Manage technical workplans, priorities, dependencies, risks, and deliverables to keep workstreams on schedule and aligned with project objectives. Serve as a bridge between business and technical teams by translating business requirements and data needs into actionable technical requirements and solutions. Lead the ingestion, mapping, transformation, validation, and integration of complex healthcare data—including claims, eligibility, enrollment, payment, and other administrative data—from multiple sources. Guide the design and development of scalable data pipelines and analytics solutions using Databricks, Python, SQL, and modern data engineering practices. Work with clients and external stakeholders to understand data structures, business rules, data-quality issues, and technical constraints, and drive issues through resolution. Oversee data validation and quality-control processes to assess completeness, accuracy, consistency, and readiness for downstream analytics. Lead technical requirements and solutioning discussions, communicating technical approaches, risks, issues, and recommendations to technical and non-technical audiences. Provide hands-on troubleshooting and technical guidance as needed while maintaining ownership of overall team delivery. Identify opportunities to improve data engineering processes, automation, maintainability, and scalability. The position requires a bachelor’s degree, although relevant experience may be substituted for formal education or an advanced degree, and at least five years of experience, preferably in healthcare or the public sector. The role requires travel up to 25% and the ability to obtain Public Trust clearance. The annual salary range is $130,000 to $216,000, with compensation decisions dependent on factors including skills, experience, training, clearances, licensure, certifications, and organizational needs.

Requirements

Bachelor’s degree; relevant experience may be substituted for formal education or an advanced degree.
At least five years of experience, preferably within healthcare or the public sector.
Experience working with large, complex datasets and translating business requirements into technical solutions.
Excellent written and verbal communication skills, including experience working directly with clients and explaining complex technical concepts to non-technical audiences.
Strong problem-solving skills and demonstrated ability to lead teams through complex technical and data issues.
Ability to manage multiple priorities, stakeholders, dependencies, and deadlines in a fast-paced environment.
Experience working with healthcare data, health IT, or healthcare analytics.
Ability to obtain Public Trust clearance.

Nice To Haves

Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Analytics, or a related field, or equivalent relevant experience.
Previous experience supporting Centers for Medicare & Medicaid Services programs, systems, or data.
Seven or more years of experience delivering data, analytics, technology, or related technical solutions, preferably within healthcare or the public sector.
Three or more years of experience leading technical teams or technical delivery, including work planning, delivery management, issue resolution, and coordination across multiple stakeholders.
Demonstrated technical project management experience delivering complex data, analytics, or technology initiatives.
Strong experience with data engineering, including data ingestion, ETL/ELT, transformation, data mapping, data validation, and data quality.
Hands-on experience with Databricks, Python, and SQL, or demonstrated experience leading teams delivering solutions using these technologies.
Experience working with state government health programs, including Medicaid, State-based Exchanges, or other health and human services programs.
Experience with healthcare claims, eligibility, enrollment, payment, or other administrative healthcare data.
Experience designing or delivering solutions using Databricks Lakehouse/Medallion architecture, Delta Lake, or comparable cloud data platforms.
Experience modernizing legacy data or analytics environments, including migration from SAS or other legacy technologies to Python, SQL, or Databricks.
Understanding of data normalization, automated validation, configurable business rules, and data-quality frameworks.
Experience working in Agile delivery environments and coordinating across engineering, analytics, functional, and project management teams.
Experience supporting federal technology or data programs with security, privacy, and data-governance requirements.

Responsibilities

Lead technical delivery for complex healthcare data and analytics initiatives and provide day-to-day direction to data engineers, analysts, and other technical team members.
Manage technical workplans, priorities, dependencies, risks, and deliverables to keep workstreams on schedule and aligned with project objectives.
Translate business requirements and data needs into actionable technical requirements and solutions.
Lead ingestion, mapping, transformation, validation, and integration of complex healthcare data from multiple sources.
Guide the design and development of scalable data pipelines and analytics solutions using Databricks, Python, SQL, and modern data engineering practices.
Work with clients and external stakeholders to understand data structures, business rules, data-quality issues, and technical constraints, and drive issues through resolution.
Oversee data validation and quality-control processes to assess completeness, accuracy, consistency, and readiness for downstream analytics.
Lead technical requirements and solutioning discussions and communicate technical approaches, risks, issues, and recommendations to technical and non-technical audiences.
Provide hands-on troubleshooting and technical guidance while maintaining ownership of overall team delivery.
Identify opportunities to improve data engineering processes, automation, maintainability, and scalability.