Data Engineer

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Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

Location: Remote (supporting EST hours)

Role Summary

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.

Key Responsibilities

Platform Migration & Modernization

• 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.

AI Gateway Engineering & Migration

• 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.

AI Evaluation Framework & Tool Development

• 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.

AI Monitoring & Observability

• 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.

BI Transformation Strategy & Implementation

• 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.

Data Engineering & Integration

• 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.

AWS Data Platform Development

• 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.

Security, Governance & Compliance

• 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.

Required Qualifications

• Bachelor's or Master's deg