Technical SME Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Technical SME Data Engineer based in the United States.

This role focuses on transforming large-scale regulatory data into actionable insights through advanced data engineering, statistical modeling, and machine learning. You will design and implement decision analysis and ML solutions within a cloud-native environment supporting federal regulatory initiatives. The position requires deep expertise in Apache Spark and large-scale data processing, along with the ability to translate organizational objectives into practical analytical solutions. You will also help ensure AI and ML work aligns with federal governance, documentation, and bias-testing requirements. Collaboration with program, policy, and technical teams will be central to communicating findings and model outcomes. This is a full-time opportunity based in McLean, Virginia, with potential remote flexibility depending on project requirements

Accountabilities:

The Technical SME Data Engineer will combine advanced data engineering and machine learning expertise to develop scalable analytical solutions while supporting regulatory, governance, and compliance objectives.

• Design, build, and implement decision analysis and machine learning models, including Decision Trees, Random Forests, Gradient Boosted Trees, Linear Regression, Collaborative Filtering, and K-Means.
• Develop large-scale data processing pipelines using Apache Spark core APIs, SparkSQL, and streaming capabilities.
• Perform data cleaning, transformation, validation, and comparative trend analysis across large datasets.
• Translate organizational goals and business requirements into practical machine learning models, statistical models, and pattern recognition solutions.
• Implement analytical solutions within cloud-native environments and support scalable data processing requirements.
• Ensure machine learning and AI initiatives comply with applicable federal AI governance policies, including documentation and bias-testing requirements.
• Contribute to open-source and community-driven solutions while maintaining source code through GitHub.
• Collaborate with program, policy, and technical stakeholders to communicate analytical findings, model results, and technical recommendations.
• Support data-driven initiatives that strengthen regulatory analysis and decision-making.
• Contribute subject matter expertise to technical discussions, solution development, and project delivery activities.

Requirements:

The ideal candidate combines strong data engineering and machine learning experience with practical expertise in cloud-native technologies, large-scale analytics, and federal governance environments.

• 5+ years of professional experience working with decision analysis and machine learning algorithms in cloud-native environments.
• Strong hands-on experience with algorithms such as Decision Trees, Random Forests, Gradient Boosted Trees, Linear Regression, Collaborative Filtering, and K-Means.
• Strong understanding of Apache Spark architecture and internals, including core APIs, SparkSQL, high-level data access tools, and Spark streaming.
• Experience performing data cleaning and developing comparative trend analyses using large-scale datasets.
• Proven ability to translate organizational goals into working machine learning models, statistical models, or pattern recognition solutions.
• Experience working with open-source and community solutions and managing source code using GitHub.
• 5+ years of experience using Amazon Web Services EMR Spark is preferred.
• Experience with Zeppelin or similar data science interpreters is preferred.
• Familiarity with federal AI governance requirements, including model documentation and bias testing.
• Prior experience supporting federal financial regulators such as CFPB, FDIC, OCC, SEC, FRB, or NCUA is preferred.
• Experience working in a federal FISMA Moderate or comparable security and compliance environment is beneficial.
• Bachelor’s degree in Mathematics, Data Science, or a similar field required; a Master’s degree in Mathematics, Data Science, or a related discipline is strongly preferred.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Ability to communicate technical findings clearly to program, policy, and technical stakeholders.
• Ability to work effectively in environments requiring security, regulatory, and governance awareness.

Benefits:

• Generous medical, dental, and vision insurance plans.
• Opportunity to work across different sectors while maintaining a stable full-time role.
• Flexible work arrangements, including remote opportunities depending on project requirements.
• Collaborative and team-oriented working environment.
• Opportunities to contribute to federal technology and regulatory initiatives.
• Exposure to large-scale data engineering, machine learning, cloud-native technologies, and AI governance.
• Inclusive workplace committed to diversity, accessibility, and equal employment opportunity.
• Opportunities to develop expertise in advanced analytics and mission-focused technology solutions.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.