Abia Khan
Washington,DC
Summary
Technology Risk, Compliance & Data Leader with 7+ years of experience supporting complex federal, financial, defense, and healthcare environments. Experienced in risk management frameworks, risk and control assessments, RCSAs, technology governance, regulatory compliance, and remediation. Strong background in NIST, GDPR, NIS2, data analytics, SQL, Python, and enterprise technology platforms. Proven ability to lead cross-functional initiatives, influence senior stakeholders, and translate complex technical and risk findings into clear, actionable recommendations.
Overview
7
7
years of professional experience
Work History
Manager, Data & AI
Guidehouse
Arlington, VA
03.2026 - Current
- Lead technology, data, operational, and compliance risk management for a large-scale financial systems modernization spanning 500+ GB of enterprise data and multiple legacy platforms.
- Develop and implement risk management frameworks, governance processes, control methodologies, and remediation strategies to identify, assess, prioritize, and mitigate enterprise technology risks.
- Facilitate risk assessments and Risk and Control Self-Assessments (RCSAs) with business owners, technology teams, architects, SMEs, and leadership to evaluate inherent risk, control effectiveness, residual risk, and remediation needs.
- Assess the design and operating effectiveness of technology and data controls, identifying control gaps, systemic weaknesses, dependencies, root causes, and emerging risks.
- Apply NIST, GDPR, NIS2, cybersecurity, privacy, and data-governance principles to evaluate risk exposure and translate requirements into practical controls and remediation actions.
- Leverage SQL, Python, dashboards, and automated testing to identify anomalies, control failures, duplicate records, data-integrity issues, and systemic risk patterns across enterprise systems.
- Develop risk registers, control matrices, KRIs, issue logs, remediation plans, governance reporting, and executive-level risk recommendations supporting risk-informed decision-making.
- Lead complex cross-functional programs and risk-remediation workstreams, synthesizing fragmented technical, regulatory, and business inputs into clear findings and recommendations for senior leadership.
Senior Data Engineer
Deloitte
Arlington, Virginia
09.2021 - 01.2026
- Supported FinCEN, NIH, and the U.S. Air Force across highly regulated financial, investigative, healthcare, defense, and enterprise technology environments.
- Led and supported technology, operational, compliance, and data risk assessments, identifying control gaps, vulnerabilities, process weaknesses, and remediation priorities across complex systems.
- Developed and implemented risk and control frameworks, including RCSAs, control assessments, risk documentation, control testing, issue management, and remediation tracking.
- Applied NIST, GDPR, NIS2, cybersecurity, privacy, and regulatory requirements to assess technology controls and develop practical risk-based recommendations.
- Built automated control testing and continuous-monitoring solutions using SQL and Python to evaluate data completeness, accuracy, consistency, exceptions, anomalies, and control effectiveness.
- Conducted root-cause and impact analysis across interconnected applications, databases, pipelines, and upstream/downstream dependencies to identify systemic technology and operational risks.
- Applied advanced analytics to financial and investigative datasets and developed dashboards that surfaced emerging risks, exceptions, trends, and issues requiring further review.
- Influenced technical and leadership stakeholders by translating complex findings into business impact, risk exposure, executive recommendations, and actionable remediation plans.
Data Science Fellow
Institute for Defense Analyses
Alexandria, VA
06.2020 - 08.2021
- Conducted advanced risk, quantitative, and data analyses supporting complex U.S. Department of Defense programs and senior decision-makers.
- Developed scalable Python and R workflows for statistical analysis, simulation, risk analysis, and decision-support activities across large defense datasets.
- Evaluated analytical methodologies, assumptions, model performance, controls, and data quality to identify limitations, vulnerabilities, and potential decision risks.
- Performed root-cause analysis to identify anomalies, recurring data issues, process weaknesses, and factors affecting analytical reliability.
- Developed analytical frameworks, performance measures, and validation approaches to assess risk, trends, outcomes, and operational effectiveness.
- Synthesized complex and fragmented technical information into structured findings, risk implications, and actionable recommendations for senior stakeholders.
- Collaborated cross-functionally with engineers, researchers, data scientists, program managers, and leadership to structure and resolve ambiguous technical challenges.
- Developed dashboards, visualizations, and executive analyses that translated complex quantitative findings into clear, risk-informed decision support.
Data Science Intern
Origent Data Sciences
Washington, District of Columbia
04.2019 - 05.2020
- Performed data, quality, and operational risk analysis across complex longitudinal healthcare and clinical-research datasets.
- Developed Python and R workflows to clean, transform, validate, and analyze sensitive data while maintaining quality, consistency, and reproducibility.
- Designed and executed data-quality controls, validation checks, and exception testing to identify incomplete, inconsistent, anomalous, and unreliable information.
- Conducted root-cause analysis of data-quality and process issues and implemented repeatable controls to reduce recurring errors.
- Performed statistical modeling, feature extraction, trend analysis, and quantitative assessments to identify patterns and potential areas of risk.
- Applied data governance, privacy, compliance, and control principles when managing sensitive healthcare and research information.
- Developed reproducible datasets, analytical reports, and visualizations that translated technical findings into actionable research and business insights.
- Partnered with researchers, technical stakeholders, and SMEs to define requirements, evaluate findings, resolve data issues, and support evidence-based decisions.
Education
Master of Science - Biomedical Engineering
Catholic University of America
Washington, DC05-2023
Bachelor of Science - Computer Science
George Washington University
Washington, DC05-2020
Skills
Technology Risk & Compliance: Risk Management Frameworks, Technology Risk, Enterprise Risk, Risk Assessments, RCSA, Control Assessments, Control Design & Effectiveness, Regulatory Compliance, Risk Remediation, Issue Management, NIST CSF, NIST RMF, NIST 800-53, GDPR, NIS2
Data Engineering: SQL, ETL/ELT, Data Pipelines, Data Modeling, Data Warehousing
Programming: Python, R, Bash
Data Platforms: Databricks, Snowflake, AWS, BigQuery, PostgreSQL
Analytics: Product Analytics, Data Visualization, Statistical Analysis, Dashboarding
Data Management: Data Quality, Data Governance, Data Lineage, Data Integration
Architecture: Scalable Data Architecture, Data Lakehouse, Structured & Semi-Structured Data
AI/ML: Generative AI, LLM Evaluation, Prompt/Context Engineering
Collaboration: Cross-Functional Leadership, Requirements Translation, Stakeholder Communication
Affiliations
- Khan, A., et al. “Statistical Analysis of GLCM Texture Features and Microstructures in SEM Images of Crassostrea virginica Exposed to Atrazine.” Proceedings of the 11th International Conference on Bioinformatics and Computational Biology, Vol. 60, pp. 170–180, 2019.
- Okada, K., Golbaz, M., Mansoor, A., Perez, G.F., Pancham, K., Khan, A., Nino, G., & Linguraru, M.G. “Severity Quantification of Pediatric Viral Respiratory Illnesses in Chest X-Ray Images.” IEEE Engineering in Medicine and Biology Society Annual International Conference, pp. 165–168, 2015.
- Mansoor, A., Perez, G.F., Pancham, K., Khan, A., Okada, K., Nino, G., & Linguraru, M.G. “Partitioned Active Shape Model With Weighted Landmarks for Accurate Lung Field Segmentation in Pediatric Chest Radiography.” International Journal of Computer-Assisted Radiology and Surgery, Vol. 10(S1), pp. S224–S226, 2015.
Timeline
Manager, Data & AI
Guidehouse
03.2026 - Current
Senior Data Engineer
Deloitte
09.2021 - 01.2026
Data Science Fellow
Institute for Defense Analyses
06.2020 - 08.2021
Data Science Intern
Origent Data Sciences
04.2019 - 05.2020
Master of Science - Biomedical Engineering
Catholic University of America
Bachelor of Science - Computer Science
George Washington University