Data Engineer

Fractal is a strategic AI partner to Fortune 500 companies focused on powering enterprise decision-making with intelligence. The Data Engineer will support a large-scale AI, data, and analytics transformation initiative by modernizing cloud platforms, migrating AI Gateway and BI capabilities, developing AI evaluation and observability frameworks, and building scalable data engineering solutions.

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 LLMOpenAI Custom GPTsAWS 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 testingAccuracy testingHallucination detectionPrompt evaluationGroundedness verificationSafety and toxicity testingBias and fairness assessmentRegression testingAgent workflow validationPerformance 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 performanceService latencyToken consumptionUser adoptionAI quality metricsCost and utilization trendsProduction 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: S3GlueLambdaRedshiftBedrockAPI GatewayIAM
• 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

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)PythonSQLREST APIs and microservicesTableauAlteryx
• 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
• 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 applicationsRAG systemsAI copilotsAgentic AI solutions
• Familiarity with: Vector databasesPrompt engineeringRetrieval-Augmented Generation (RAG)AI observability platformsAgent 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