Platform Engineer

• Be a part of transformational change where integrity matters, success inspires and great teams collaborate and innovate.
• Contribute toward the success of our technology initiative developing a modern analytics platform for the business.
• Deep understanding of at least one major cloud provider (Azure or AWS) regarding networking (VNet/VPC, PrivateLink), storage (ADLS Gen2, S3), and IAM.
• Advanced Proficiency in at least one programming language.
• Extensive experience with Terraform and the Databricks Terraform Provider to manage workspace resources, clusters, and permissions.
• Extensive Experience building and using automation pipelines using GitLab or similar for CICD and Databricks Asset Bundles (DABs).
• Responsible for designing, developing, testing, operating and maintaining products.
• Takes full stack ownership by consistently writing production-ready and testable code.
• Makes sound design/coding decisions keeping customer experience in the forefront.

Senior Data Analytics Platform Engineer, Databricks

• Lead design and development of enterprise integration ecosystems including API, Model Context Protocol (MCP), Agent-to-Agent (A2A), and AI Agent-driven patterns
• Enable external and internal partner connectivity through scalable, secure B2B and B2B2C integration patterns that power TIAA's partnership ecosystem
• Drive context engineering — designing how AI agents consume and act on integration capabilities — by converting APIs into agent-ready tools
• Deliver self-service integration capabilities for both human and AI consumers
• Modernize complex legacy enterprise platforms using AI-first architectural approaches

Senior Integration Platform Engineer, AI & Agentic

• Lead an engineering organization of approximately 40–60 engineers and contractors, including engineering managers, with accountability for staffing, performance, and delivery outcomes.
• Own operational and delivery results for mission-critical platforms, with a strong focus on stability, resiliency, availability, and regulatory compliance.
• Provide technical leadership across mainframe and distributed systems, supporting teams during complex delivery challenges, production incidents, and escalations.
• Drive execution of large, high-visibility initiatives, ensuring predictable delivery, transparency, and effective risk management across competing priorities.
• Lead and advance modernization efforts, including modernization on or off the mainframe, while maintaining uninterrupted business operations.
• Strengthen engineering practices through sound design decisions, quality standards, and a culture of continuous improvement.
• Develop and coach engineering managers and senior engineers, with a focus on career development, upskilling, and succession planning.
• Build strong partnerships with product, architecture, operations, and business leaders to align technology delivery with business priorities.
• Champion agile and product-oriented delivery models while maintaining accountability for results.

Software Engineering Director – Card Platform Engineering

• Lead or participate in high-level technical concepts spanning market data technology platforms and business consumption models
• Design, build, and enhance complex market data infrastructure, including LSEG TREP and related distribution systems
• Develop specifications, architectures, and solutions for low-latency, high-availability market data delivery platforms
• Contribute to the testing and validation of market data flows, including feed handlers, distribution layers, and downstream consumers
• Drive improvements in resiliency, failover, and recovery capabilities of market data systems
• Analyze and resolve issues related to real-time data delivery, including feed gaps, latency, stale data, and symbology mismatches
• Design and implement automation using UNIX/Linux scripting to monitor, manage, and maintain infrastructure health
• Collaborate with network engineering teams to ensure optimal multicast/unicast data distribution, bandwidth utilization, and low-latency performance
• Review and analyze infrastructure security considerations, including access controls, secrets management, and key rotations
• Design, code, test, debug, and document tools and utilities to support market data operations and engineering functions
• Support complex system upgrades, including TREP components, feed handlers, and supporting middleware
• Identify and resolve recurring operational issues by implementing long-term engineering solutions
• Develop and execute strategic plans to modernize and optimize market data platforms
• Direct daily risk and control processes, ensuring adherence to enterprise policies, procedures, and regulatory standards
• Collaborate and consult with peers, application teams, vendors, and leadership to resolve issues and deliver scalable, reliable solutions

Market Data Platform Engineer

• Designs, implements, and owns enterprise collaboration platforms such as Zoom or Google Workspace to meet business, regulatory, and operational requirements
• Identifies systemic gaps and opportunities to improve resiliency, performance, and user experience
• Defines and enforces platform architecture and technical standards
• Ensures alignment with enterprise controls including firewall/proxy rules, certificates/TLS, conditional access, logging, and monitoring
• Leads end-to-end implementation initiatives from requirements gathering to operational handoff
• Manages change control processes and provides post-deployment support
• Evaluates platform capabilities, vendor offerings, and roadmaps with consideration for security, compliance, scalability, availability, and user experience
• Recommends platform improvements and drives adoption of strategic capabilities
• Develops and maintains operational documentation
• Mentors junior engineers and provides escalation support
• Performs systems analysis, including requirements definition and logical/physical design for collaboration platforms
• Ensures solutions meet enterprise risk, compliance, and availability expectations
• Participates in special projects and cross-functional initiatives supporting collaboration platform reliability, scalability, and operational excellence.

Senior Collaboration Platform Engineer, Zoom – Google Workspace

• Lead complex initiatives to develop infrastructure to provide solutions for business applications
• Participate in various projects intended to continually improve or upgrade the infrastructure
• Evaluate internal and external software solutions which could be leveraged to meet target state architecture goals
• Review and analyze high impact outages to ensure the proper processes and procedures are in place to avoid problems in the future
• Design, build, deploy and maintain infrastructure solutions through collaborative efforts with the team and third party vendors
• Design, code, test, debug and document programs using Agile development practices
• Make decisions in technical designs, implementation plans and identify project risks and resource requirements
• Direct the daily risk and control flow of operations, focusing on policies, procedures and work standards to ensure success
• Recommend courses of action to maintain cost effectiveness and achieve results
• Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals
• Interact with customer and vendor

Lead Infrastructure Platform Support Engineer

• Design and build scalable, distributed data platforms on AWS supporting AI and analytics workloads
• Develop and optimize modern data storage systems using technologies like Apache Iceberg and Databricks
• Build cloud-native platform services and applications, primarily in Python
• Design and maintain CI/CD pipelines and automated deployment workflows using GitHub
• Operate and improve production cloud environments, ensuring high availability, performance, and reliability
• Troubleshoot and resolve complex issues across data platforms, infrastructure, and applications
• Implement automation and infrastructure-as-code solutions to improve scalability and efficiency
• Optimize storage performance, data access patterns, and system throughput
• Contribute to disaster recovery, backup strategies, and system resilience
• Establish and enforce engineering standards, configuration best practices, and operational processes
• Partner with engineering and product teams to deliver quarterly platform capabilities and roadmap initiatives
• Mentor engineers and contribute to a high-performing, collaborative team environment
• Work with modern AI-assisted development tools including Copilot and Claude

Senior Platform Engineer – Data Platform

• Responsible for Alteryx platform management, platform support and administration.
• Focus on ensuring smooth user experiences and maintaining platform stability.
• Help drive adoption of Alteryx across the organization.
• Develop and maintain deployment pipelines for the platform.
• Maintain and ensure operational metadata are available for diagnostics.
• Implement effective technical solutions and provide problem analysis and resolution in a timely manner.
• Build non-functional monitoring capabilities for complex applications in production.
• Lead small data engineering projects with manageable risks and resource requirements.

Data Platform Engineer – Alteryx Platform Owner

• Design, build, and deploy Microsoft Copilot Studio agents—including topics, knowledge sources, generative orchestration, authentication, and content moderation—that automate enterprise workflows across the bank
• Develop and maintain custom Power Platform connectors and MCP (Model Context Protocol) servers that securely expose internal banking systems and APIs to Copilot Studio agents and Power Platform makers
• Architect and govern Dataverse solutions—tables, relationships, security roles, business rules, plug-ins, and elastic tables—serving as the system of record for Copilot Studio agents and Power Apps
• Build agent tools, plug-ins, and reusable components in C# and .NET 10 to extend Copilot Studio with custom business logic, API integrations, and AI orchestration patterns (tool calling, RAG, declarative agents)
• Operate Power Platform Application Lifecycle Management (ALM) at scale—managed environments, solution packaging, the ALM Accelerator, Power Platform CLI (pac), and pipelines integrated with GitHub Actions
• Implement responsible AI governance—DLP policies, environment strategy, agent telemetry, evaluation harnesses, content safety, and grounding—ensuring compliance with bank security and regulatory standards
• Partner with software, data, and AI engineers to integrate Azure OpenAI, Azure AI Foundry, and Azure AI Search into Power Platform and Copilot Studio solutions, including knowledge ingestion and retrieval pipelines
• Mentor pro-code and citizen developers on Copilot Studio patterns, Power Platform ALM, secure coding, and AI engineering best practices; champion compliance with change management and SDLC standards.

AI Platform Engineer

• Provision and manage cloud infrastructure across compute, networking, storage, and managed databases
• Maintain consistency across development, staging, and production environments
• Plan and execute workload migrations across cloud providers with minimal disruption
• Identify and implement infrastructure cost optimization opportunities
• Build and maintain CI/CD pipelines across a multi-repository environment
• Implement deployment strategies such as blue/green, canary, and rolling deployments
• Develop automation tooling and scripts to reduce manual effort and improve delivery speed
• Write and maintain Terraform infrastructure across multi-account, multi-environment systems
• Establish IaC standards, module reuse patterns, and state management practices
• Drive infrastructure changes through version-controlled, peer-reviewed workflows
• Participate in on-call rotation and lead incident response efforts
• Maintain observability through dashboards, alerting, logging, and SLO tracking
• Conduct post-incident reviews and implement long-term remediations
• Manage secrets, certificates, and access controls using least-privilege principles
• Apply security best practices across infrastructure, containers, and CI/CD pipelines
• Support audit and compliance initiatives through documentation and access reviews
• Partner with engineering teams on infrastructure guidance and service design
• Create architecture documentation, runbooks, and operational guides
• Help build self-service platform capabilities for engineering teams

Senior Platform Engineer

• Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos), with ownership of control plane, etcd, and cluster lifecycle.
• Architect scalable, multi-tenant platform infrastructure as the foundation for AI/ML and GenAI workloads.
• Enable and optimize AI/ML workloads, including GPU-based environments for training, inference, and model deployment.
• Partner with data scientists and ML engineers to onboard and scale ML pipelines and workflows.
• Build platform capabilities using Kubernetes controllers, operators, CRDs, and Golang/Python services.
• Implement Infrastructure as Code, automation, and AIOps-driven self-healing using platform telemetry and observability.
• Ensure reliability through performance tuning (scheduling, resource utilization) and participate in on-call support and incident response.

Kubernetes Platform Engineer – AI Infrastructure

• Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos)
• Architect scalable, multi-tenant platform infrastructure as the foundation for AI/ML and GenAI workloads
• Enable and optimize AI/ML workloads, including GPU-based environments for training, inference, and model deployment
• Partner with data scientists and ML engineers to onboard and scale ML pipelines and workflows
• Build platform capabilities using Kubernetes controllers, operators, CRDs, and Golang/Python services
• Implement Infrastructure as Code, automation, and AIOps-driven self-healing using platform telemetry and observability
• Ensure reliability through performance tuning (scheduling, resource utilization) and participate in on-call support and incident response

• Architect, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos)
• Define and evolve scalable, multi-tenant platform architecture supporting AI/ML and GPU-based workloads
• Enable and optimize ML workloads including training, inference, and LLM deployment pipelines on Kubernetes
• Build platform extensions using Kubernetes controllers, operators, CRDs, and Golang-based services
• Implement Infrastructure as Code and automation to improve scalability, consistency, and operational efficiency
• Drive AIOps capabilities using telemetry, automation, anomaly detection, and self-healing systems for platform reliability
• Improve observability (metrics, logs, traces) and optimize resource utilization, scheduling, and cluster performance
• Partner with ML engineers and data scientists to operationalize ML workflows and ensure platform readiness for AI workloads
• Participate in on-call rotations, owning incident response, reliability, and continuous operational improvement
• Mentor engineers and contribute to defining platform engineering standards and best practices

Senior Kubernetes Platform Engineer – AI Infrastructure

• Architect, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos), including control plane and etcd lifecycle management
• Define and evolve scalable, multi-tenant platform architecture supporting AI/ML and GPU-based workloads
• Enable and optimize ML workloads including training, inference, and LLM deployment pipelines on Kubernetes
• Build platform extensions using Kubernetes controllers, operators, CRDs, and Golang-based services
• Implement Infrastructure as Code and automation to improve scalability, consistency, and operational efficiency
• Drive AIOps capabilities using telemetry, automation, anomaly detection, and self-healing systems for platform reliability
• Improve observability (metrics, logs, traces) and optimize resource utilization, scheduling, and cluster performance
• Partner with ML engineers and data scientists to operationalize ML workflows and ensure platform readiness for AI workloads
• Participate in on-call rotations, owning incident response, reliability, and continuous operational improvement
• Mentor engineers and contribute to defining platform engineering standards and best practices

Senior Kubernetes Platform Engineer – AI/ML Infrastructure

• Architect, deploy, and provide senior-level operational support for our on-premise and cloud-based Linux infrastructure and core IT services (e.g., virtualization, baremetal, storage, DNS), ensuring high availability and reliability.
• Develop, maintain, and champion our Infrastructure-as-Code (IaC) and automation frameworks using tools like Terraform, Ansible, and Foreman/MaaS to manage and deploy platform services.
• Implement and automate system-level security best practices, including patching, hardening, and configuration management, ensuring compliance and resilience from the ground up.
• Build and automate deployment pipelines for IT infrastructure services (e.g., system images, configuration, platform services) using tools like GitHub/Git, Ansible, and scripting tools.
• Serve as a technical Subject Matter Expert (SME), working with IT Systems, CloudOps, Security, and Engineering teams to design and implement robust, scalable, and optimal solutions.
• Participate in a shared on-call rotation to support mission-critical IT services (with clear documentation and runbooks provided).
• Create and maintain accurate documentation for automation, operational audits, and compliance.
• Design, implement, and administer enterprise storage platforms, including Dell PowerStore and Pure Storage arrays, ensuring capacity, performance, data protection, and high availability for critical workloads.
• Mentor junior team members.

Senior Staff Platform Operations Engineer

• Own day-to-day platform operations across AWS infrastructure and Kubernetes environments.
• Improve reliability, scalability, and performance of systems supporting our learning platform.
• Operate and evolve multiple EKS clusters across production and non-production environments.
• Build internal self-service tooling and engineering guardrails that help developers ship safely and independently.
• Improve CI/CD workflows using GitHub Actions and GitOps practices with ArgoCD.
• Drive observability improvements using DataDog, including dashboards, alerting, and incident response readiness.
• Partner with software engineers, QA, product, and security teams to remove friction and increase delivery velocity.
• Lead infrastructure changes end-to-end using Terraform and modern IaC practices.
• Reduce operational toil through automation, standardization, and thoughtful platform design.
• Participate in an on-call rotation focused on ownership, reliability, and continuous improvement.
• Mentor teammates through collaboration, code review, and operational best practices.

• Design, build, and operate scalable ML platform components including training infrastructure, feature stores, model registries, inference services, and end‑to‑end workflow orchestration.
• Develop cloud‑native, distributed systems and CI/CD pipelines that ensure reliable, reproducible, and continuously delivered ML model deployments.
• Implement and mature MLOps capabilities such as experiment tracking, data and model versioning, model evaluation, monitoring, and automated retraining.
• Establish best practices for model lifecycle management, testing, and deployment across development, staging, and production environments.
• Integrate observability into ML systems, enabling deep visibility into performance, drift, data quality, and inference reliability.
• Build and optimize cloud-based ML infrastructure on Azure, AWS, and/or GCP using Kubernetes, container orchestration, and infrastructure‑as‑code tools.
• Develop scalable batch and real‑time data pipelines that power feature generation, training workflows, and high‑performance model serving.
• Ensure security, compliance, and cost-effectiveness across ML environments in partnership with platform, architecture, and governance teams.
• Collaborate with data scientists and applied ML teams to translate modeling needs into robust, reusable, and self-service platform capabilities.
• Work with security, compliance, and architecture partners to uphold responsible AI, governance, and data protection standards.
• Drive developer productivity by promoting self‑service tooling, reusable components, documentation, and engineering best practices.
• Contribute to Agile delivery processes while championing automation, engineering excellence, and continuous improvement.

Machine Learning Platform Engineer

• As a Senior Data Platform Engineer, you’ll be a hands-on engineer on a small, high-ownership data team.
• You’ll work across the full data platform - relational, warehouse, and lakehouse systems - building and operating the pipelines that power compliance, analytics, and reporting workloads.
• Design and build pipelines that move data across systems - supporting data lake ingestion, compliance workloads, and cross-domain data flows.
• Own pipeline operations end to end: monitoring, incident resolution, data quality, and documentation that lets any team member respond independently.
• Identify technical debt and reliability risks and raise them with clear context and proposed next steps.
• Design and maintain schemas across relational, warehouse, and lakehouse layers, working with application engineers and product to get data models right.
• Build out the platform’s service layer, infrastructure-as-code, and data quality frameworks - this role spans design and implementation.
• Keep platform documentation at a level where any team member can understand what exists, how it works, and where the risks are.
• Over time, contribute to the analytics engineering layer, including modeling practices and semantic layer development.
• Contribute to evaluations of the current platform against emerging architectures and tooling, helping produce trade-off analyses and recommendations.
• Bring what you see day to day in the systems you operate into the team’s improvement roadmap and technical direction.
• Track and report on platform health metrics: pipeline uptime, failure rates, data freshness, and cost trends.
• Mentor peers and junior engineers through code review, pairing, and technical guidance.

Senior Data Platform Engineer

• Act as an advisor to senior leadership to develop or influence analytical strategies for highly complex business and technical needs
• Lead the strategy and resolution of highly complex and unique challenges requiring analytical application of industry techniques and advanced data from multiple sources across the enterprise
• Deliver solutions that are long-term, large-scale, and require vision, innovation, and coordination of highly complex activities
• Provide vision, direction, and expertise to senior leadership on implementing innovative and significant business solutions that align to the enterprise
• Ensure adherence to compliance and legal regulations and policies on all projects managed
• Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership

Senior Lead Microsoft Power Platform Engineer

• Lead the technical design and enablement of RTM use cases, focusing heavily on RTCDP
• Work with cross-functional teams to ensure high-fidelity behavioral data is captured and mapped correctly into AEP
• Develop a future-proof technical roadmap for AEP that prioritizes scalability and rapid deployment of new CX innovations.
• Define the "gold standard" for AEP schemas (XDM), identity namespaces, and profile merge policies
• Monitor dataflow health and ingestion patterns; proactively identify and resolve technical bottlenecks
• Manage the technical lifecycle of the platform, including feature adoption, sandbox management, and seamless upgrades
• Act as the primary technical point of contact for Adobe, influencing their product roadmap
• Partner with IT and Security to ensure the MarTech stack is architecturally sound.
• Develop technical KPIs to track platform uptime, data latency, and efficiency of audience activation.

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