Senior Data Science Platform Engineer, (Senior Consultant, Data & Analytics)

Fearless•Baltimore, MD

1d•$120,588 - $195,955•Remote

About The Position

At Fearless, we are seeking a Senior Data Science Platform Engineer as part of our Senior Consultant, Data and Analytics career path to support the production data science platform. This individual will engineer scalable AWS environments for advanced analytics, machine learning, and AI-assisted research while maintaining operational excellence and supporting future platform enhancements. At Fearless, the Senior Consultant is an expert in data analysis, engineering, and science. This role will possess the necessary skills at an advanced level of proficiency to drive organizational transformation by architecting scalable data platforms, building advanced machine learning solutions, and mentoring customers and teams to become data-driven organizations. The role involves working with executives, project teams, and internal leaders across departments to identify opportunities where data can accelerate missions, create efficiencies, and deliver technical excellence.

Requirements

Minimum of 10 years of experience designing enterprise data architectures, with at least 5 years focused on cloud-native data platforms.
Experience working at or in support of the U.S. Securities and Exchange Commission (SEC), with knowledge of SEC business processes, systems, and regulatory mission.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field, or equivalent work experience.
Ability to obtain and maintain Public Trust clearance.
Experience as an AWS Solutions Architect designing and implementing production systems.
Proven experience with AWS Glue, SageMaker, FSx, and S3 in production data workflows.
Proficiency in Python, R, and SQL for data pipeline development and analytics support.
Experience working in Linux environments and managing infrastructure as code.
Experience with GitHub for version control and collaborative development.
Experience operating in a DevSecOps environment with automated security controls and compliance monitoring.
Experience extracting and transforming data within AI and ML platforms for research and analytics.
Experience with Agile Scrum methodologies supporting iterative platform delivery.

Nice To Haves

Experience supporting Treasury, IRS, CFPB, FDIC, OCC, Federal Reserve, or similar regulatory agencies.
AWS Machine Learning Specialty or AWS Data Engineer Associate certification.
Experience working with multiple stakeholders to mentor technical teams and drive collaborative outcomes.

Responsibilities

Collaborate with business stakeholders to translate analytical and research requirements into scalable technical solutions.
Maintain Amazon SageMaker environments supporting machine learning workflows and model development.
Support JupyterLab, RStudio, Plotly Dash Enterprise, and Quarto platforms for data science teams.
Engineer scalable shared storage using Amazon FSx and S3 to enable cross-team data access.
Develop AWS cross-account data pipelines to securely move and transform data across organizational boundaries.
Build CI/CD automation to streamline platform deployments and reduce manual overhead.
Optimize cloud costs and monitor usage anomalies to ensure responsible stewardship of federal resources.
Produce SOPs, architecture documentation, and operational runbooks to support platform sustainability and knowledge transfer.
Support AI-enabled analytics platforms by maintaining infrastructure that enables data scientists and analysts to develop, test, and deploy machine learning and artificial intelligence solutions.
Develop test automation frameworks and scripts to validate infrastructure deployments, data pipelines, and system configurations.
Support operations and maintenance (O&M) activities, including user assistance, incident response, system monitoring, and routine platform maintenance.
Ensure compliance with organizational security standards and cloud governance policies by implementing security controls, conducting audits, maintaining access controls, and protecting sensitive data.