Data Linkage Privacy Lead

Overview

Req : RQ213583

Type of Requisition: Regular

Clearance Level Must Be Able to Obtain: None

Public Trust/Other

Required:

BI Full 6C (T4)

Job Family: Data Science and Data Engineering

Skills: Collaborating,Data Privacy,Data Tokenization,Encryption,Record Linkage

Experience: 7 + years of related experience

Job Description: GDIT is seeking an experienced Privacy Preserving Record Linkage and Automation (PPRL-A) Technical Lead to serve as the hands‑on technical lead for privacy‑preserving record linkage automation within secure, cloud‑hosted environments supporting a large federal data access and linkage initiative. This role leads design, implementation, and continuous improvement of automated, AI/ML‑enabled PPRL solutions that link large‑scale federal and non‑federal data sources while maintaining strict privacy and security controls.

The PPRL-A Technical Lead reports to the PPRL-A Program Manager and partners closely with data linkage, enclave, and program operations teams to ensure PPRL workflows are accurate, scalable, explainable, and integrated into operational linkage pipelines.

How a PPRL-A Technical Lead Will Make an Impact

• Lead design and implementation of PPRL architectures, algorithms, and workflows that support automated matching across multiple data sources with strong privacy protection and linkage quality.
• Develop and refine AI/ML‑enabled matching strategies, blocking schemes, and scoring methods, and ensure methods are documented, reproducible, and explainable for federal stakeholders.
• Integrate PPRL pipelines with enclave and linkage platforms, including data ingestion, encryption, key management, job orchestration, and monitoring in secure, cloud‑hosted environments.
• Define and oversee quality assurance processes for PPRL, including error assessment, linkage validation, and production of methodology and performance reports.
• Collaborate with privacy, security, and policy leads to ensure PPRL implementations align with applicable regulations, DUAs, governance decisions, and program risk tolerances.
• Implement dashboards and metrics to track PPRL workloads, runtimes, match quality, and system performance, and drive continuous improvement based on operational and research needs.
• Provide technical leadership, reviews, and mentoring for PPRL and linkage engineers and analysts, establishing reusable patterns, templates, and tools.

What You’ll Need to Succeed

• Doctoral degree in a relevant field such as computer science, data science, biostatistics, epidemiology, bioinformatics, health services research, or a closely related discipline.
• Deep knowledge of privacy‑preserving record linkage methodologies and probabilistic and deterministic record linkage, including blocking, matching, scoring, and evaluation of linkage quality.
• Hands‑on experience implementing PPRL, linkage workflows, and automations in regulated data environments, preferably with health or administrative data at large scale.
• Strong programming and data engineering skills in languages and platforms commonly used for large‑scale data processing and linkage.
• Solid understanding of federal data security and privacy expectations, including handling of PII, tokenization, encryption, and de‑identification in support of research use.
• Broad technical background sufficient to work effectively with enclave, security, and data engineering teams in cloud‑hosted environments.
• At least 7 years of experience conducting and leading large‑scale linkage or PPRL projects for federal agencies, research organizations, or healthcare systems, including technical‑lead responsibilities.
• Excellent written and verbal communication skills, including clear documentation of methods and findings for technical and non‑technical stakeholders.
• Ability to obtain and maintain a Public Trust or higher and authorization to work in the United States.
• Willingness to travel 10–25% to support on‑site collaboration in the DC Metro area.

Preferred

• Experience designing or deploying fully automated PPRL solutions that integrate AI/ML, encryption, and scalable matching services in cloud environments.
• Prior work with federal health or research agencies on data linkage, PPRL, and real‑world…