Data Engineer

Fractal Analytics•New Jersey, NJ

3d•$115,000 - $125,000•Remote

About The Position

We are seeking an experienced Data Engineer to support a large-scale AI, Data, and Analytics transformation initiative within a leading Financial Services organization. This role will be responsible for driving platform modernization, AI Gateway migration, BI transformation, AI evaluation framework development, and cloud-native data engineering. The successful candidate will play a key role in supporting the organization's AI ecosystem built on Claude LLM, OpenAI Custom GPTs, AWS, Tableau, and Alteryx, while helping define strategy and execute the migration of enterprise AI and analytics platforms.

Requirements

Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related discipline.
5+ years of experience in Data Engineering, Platform Engineering, Cloud Engineering, or Analytics Engineering.
Strong experience delivering cloud migration and platform modernization initiatives.
Hands-on expertise with: AWS (S3, Glue, Lambda, Redshift, Bedrock, API Gateway, IAM), Python, SQL, REST APIs and microservices, Tableau, Alteryx.
Experience integrating applications with Claude, OpenAI, or similar LLM platforms.
Experience designing and implementing AI testing, evaluation, and monitoring solutions.
Strong understanding of data architecture, ETL/ELT development, and cloud-native engineering.
Experience supporting enterprise-scale platform migration programs.
Knowledge of data governance, security, and compliance requirements.
Excellent analytical, problem-solving, and stakeholder management skills.

Nice To Haves

Experience building or managing enterprise AI Gateway platforms.
Experience leading migration initiatives from Portkey, Kong, MuleSoft, Apigee, or similar API/AI Gateway solutions.
Experience defining AI evaluation frameworks for: LLM applications, RAG systems, AI copilots, Agentic AI solutions.
Familiarity with: Vector databases, Prompt engineering, Retrieval-Augmented Generation (RAG), AI observability platforms, Agent orchestration frameworks.
Experience supporting Alteryx-to-cloud modernization programs.
Experience implementing Tableau Cloud migrations and BI transformation initiatives.
Financial Services domain experience across Banking, Capital Markets, Asset Management, Wealth Management, or Insurance.
AWS Data Engineer, Solutions Architect, or Machine Learning certifications.
Exposure to AI governance and model risk management frameworks.

Responsibilities

Design and execute migration strategies for enterprise data, analytics, and AI platforms to AWS cloud environments.
Assess legacy architectures and define target-state data and AI platform architectures.
Lead migration planning, implementation, testing, and production deployment activities.
Optimize platform performance, scalability, resiliency, and operational efficiency.
Develop migration roadmaps and implementation plans aligned with business and technology objectives.
Design, develop, and support enterprise AI Gateway solutions enabling governed access to AI services.
Lead the migration of AI Gateway capabilities from Portkey to Apigee, including architecture design, implementation planning, and execution.
Develop and maintain integration frameworks connecting enterprise applications to: Claude LLM, OpenAI Custom GPTs, AWS Bedrock and AI services.
Implement authentication, authorization, observability, rate limiting, usage tracking, and audit capabilities.
Create reusable APIs and services that enable secure and scalable AI adoption across the organization.
Establish operational monitoring and governance controls for AI platform usage.
Define enterprise AI evaluation, testing, and validation frameworks for GenAI applications, AI agents, and LLM-powered solutions.
Build evaluation tooling and automation capabilities covering: Functional testing, Accuracy testing, Hallucination detection, Prompt evaluation, Groundedness verification, Safety and toxicity testing, Bias and fairness assessment, Regression testing, Agent workflow validation, Performance benchmarking.
Develop evaluation datasets, benchmark suites, scoring methodologies, and approval criteria.
Implement automated testing pipelines integrated into AI development and deployment workflows.
Create evaluation dashboards and reporting capabilities for engineering, governance, and business stakeholders.
Design and implement monitoring frameworks for AI applications, agents, and AI Gateway services.
Develop solutions to monitor: Model performance, Service latency, Token consumption, User adoption, AI quality metrics, Cost and utilization trends, Production incidents and failures.
Implement alerting, troubleshooting, and operational support processes.
Build observability dashboards supporting engineering, operations, and governance teams.
Define strategy and implementation roadmap for enterprise BI modernization initiatives.
Support Alteryx retirement and migration of analytics workflows to strategic cloud-based platforms.
Lead migration strategy and execution for Tableau Cloud adoption.
Assess existing reporting, dashboards, workflows, and dependencies to establish migration priorities.
Design scalable and governed analytics architectures supporting self-service business intelligence.
Collaborate with business and analytics teams to modernize reporting ecosystems and improve user adoption.
Build and maintain scalable ETL/ELT data pipelines supporting AI, analytics, and reporting workloads.
Design ingestion, transformation, and orchestration frameworks for structured and unstructured datasets.
Enable creation of high-quality data products for AI model development, evaluation, and business intelligence.
Implement data quality, metadata management, lineage, and governance capabilities.
Support integration between enterprise applications, AI platforms, and analytics tools.
Develop and optimize cloud-native solutions leveraging AWS technologies including: S3, Glue, Lambda, Redshift, Bedrock, API Gateway, IAM.
Implement CI/CD, infrastructure automation, and deployment best practices.
Support platform engineering efforts ensuring secure, scalable, and compliant deployments.
Implement data governance, security, privacy, and compliance requirements across AI and analytics platforms.
Ensure adherence to Financial Services regulatory standards and enterprise risk controls.
Support audit readiness through monitoring, documentation, and operational transparency.
Enable secure handling of customer, financial, and sensitive business data.