Platform Engineer

• Define and execute the enterprise Platform Engineering strategy supporting Consumer Products and Corporate Technology.
• Lead the evolution of engineering platforms that improve developer experience, platform consistency, scalability, and operational efficiency.
• Establish enterprise standards for platform architecture, engineering automation, and software delivery.
• Drive adoption of Internal Developer Platforms (IDPs), Infrastructure as Code, cloud platform engineering, Kubernetes, container platforms, release engineering, environment management, artifact repositories, secrets management, observability platforms, and platform networking.
• Build reusable engineering capabilities that enable product teams to deliver software securely and efficiently.
• Leadership, People Management, Budget & Vendor Management

Senior Director, Autonomous Platform Engineering

• Own the technical roadmap for Platform Engineering.
• Architect, build, and operate highly scalable backend services using Ruby on Rails.
• Design and operate our Kubernetes infrastructure, cloud architecture, and deployment pipelines.
• Improve developer productivity through CI/CD, automation, internal tooling, and engineering best practices.
• Build and evolve the platform powering our AI products, including LLM orchestration, agent infrastructure, prompt management, evaluation pipelines, model routing, and AI observability.
• Own our billing platform, including subscriptions, licensing, payments, usage metering, invoicing, and Stripe integrations.
• Design and maintain secure, scalable integrations with HRIS platforms, identity providers, and enterprise customers using REST APIs, OAuth, SAML, SCIM, and webhooks.
• Build best-in-class observability through logging, metrics, tracing, alerting, and performance monitoring.
• Improve the scalability, security, reliability, and operational excellence of our platform.
• Lead architectural decisions that impact multiple engineering teams.
• Mentor engineers through technical leadership, design reviews, and engineering best practices.
• Diagnose and resolve complex production issues across infrastructure, applications, and distributed systems.
• Help establish and grow the Platform Engineering function as EasyLlama scales.

Staff Software Engineer – Platform

• Lead the support, administration, reliability, and day-to-day operations of enterprise cybersecurity, SIEM, observability, log management, and data platforms
• Support the operational roadmap for cybersecurity platform capabilities
• Provide technical leadership and mentorship for global support teams
• Optimize platform operations by using monitoring alerts, performance trends, service metrics, and operational insights
• Ensure compliance with enterprise security, audit, regulatory, and data lifecycle management requirements

Senior Platform Operations Engineer

• Design and implement scalable workflow orchestration platforms using technologies such as Temporal, Azure Durable Functions, Camunda, or similar workflow engines.
• Build long-running, fault-tolerant workflows supporting underwriting, borrower onboarding, fulfillment, servicing, and operational lending processes.
• Develop durable execution patterns including retries, checkpointing, recovery mechanisms, asynchronous coordination, and workflow resiliency.
• Design distributed event-driven architectures utilizing Azure Service Bus, Event Grid, APIs, and messaging services.
• Build highly scalable AI execution infrastructure capable of orchestrating complex multi-step business workflows.
• Continuously optimize workflow performance, scalability, and operational efficiency across AI-enabled platforms.
• Architect and deploy cloud-native applications using Microsoft Azure services.

A.I. Platform Engineer

• Design and build the next-generation bare metal deployment pipeline to achieve sub-60-second provisioning times
• Develop and maintain Vultr’s bare metal automation and management toolkit
• Implement automated firmware update workflows across the bare metal fleet using IPMI/BMC protocols
• Build and optimize kexec-based fast boot and provisioning flows to minimize deployment latency
• Integrate cloud-init and instance metadata service for bare metal, enabling consistent configuration across VM and BM products
• Develop Puppet and Ansible modules for bare metal server configuration and lifecycle management
• Architect and implement VPC networking for bare metal instances, including network attach and routing automation
• Collaborate with the Provisioning and Orchestration teams to unify OS image pipelines and ensure bare metal integrates cleanly with Vultr’s reservation and assignment systems

Platform Engineer – Bare Metal

• Lead the technical design and enablement of RTM use cases, focusing heavily on RTCDP for profile orchestration, segment creation, and real-time activation.
• Work with cross-functional teams to ensure high-fidelity behavioral data is captured and mapped correctly into AEP to power immediate, personalized customer journeys.
• Develop a future-proof technical roadmap for AEP that prioritizes scalability and the rapid deployment of new CX innovations.
• Define the 'gold standard' for AEP schemas (XDM), identity namespaces, and profile merge policies to ensure data remains clean and usable.
• Monitor dataflow health and ingestion patterns; proactively identify and resolve technical bottlenecks before they impact marketing performance.
• Manage the technical lifecycle of the platform, including feature adoption, sandbox management, and ensuring seamless upgrades with minimal disruption to the business.
• Act as the primary technical point of contact for Adobe, influencing their product roadmap and ensuring Verizon’s technical needs are prioritized.
• Partner with IT and Security to ensure the MarTech stack is architecturally sound, while acting as a subject matter expert for the dedicated Governance/Compliance team.
• Develop technical KPIs to track platform uptime, data latency, and the efficiency of audience activation.

Adobe Experience Platform Engineer

• Design, implement, and maintain CI/CD pipelines using modern DevSecOps practices, including Git-based workflows, artifact management, security controls integration, and containerized workloads (Docker/Kubernetes)
• Develop automation solutions using scripting languages or AI platforms to automate operational tasks to improve platform reliability and efficiency
• Provide platform and DevOps support for a graph database application, including deployments, upgrades, configuration management, and environment maintenance
• Troubleshoot infrastructure issues across storage, networking, load balancers, firewalls, and security groups, analyzing logs and metrics to identify root causes and recommend solutions
• Document platform best practices, promote operational standards, and support incident response and problem resolution efforts

Platform Engineer – Neo4j

• Design, build, and operate a secure, on-premise analytics and AI platform
• Own end-to-end technical delivery—from data ingestion and modeling to AI-driven analytics
• Design and implement secure data pipelines to migrate and unify data from PostgreSQL, DynamoDB, and other source databases into Snowflake
• Build and optimize ELT/ETL workflows, data models, and schemas in Snowflake
• Ensure high data quality and reconciliation between source systems and Snowflake
• Build analytics datasets and support enterprise reporting
• Enable self-service analytics for business and operations teams
• Implement private/self-hosted LLM architectures with secure inference pipelines
• Develop ML pipelines for feature engineering, training, validation, and inference
• Expose secure APIs for data access, analytics, and AI inference.
• Enforce data security and governance across PostgreSQL, Snowflake, and AI platforms.

Principal Data & AI Platform Engineer

• Own Platform's company-level objectives and the engineering plan that delivers them
• Translate company priorities and product-engineering needs into a platform roadmap, in a way that flows both up and down
• Be responsible for build-vs-buy and architecture-level trade-offs across infrastructure, data, developer experience, and AI tooling; own the technical roadmap and how it balances against the needs of your customers
• Represent Platform and collaborate with cross-functional leadership partners and in engineering-wide leadership conversations
• Coach EMs to a higher bar with clear feedback, growth plans, and direct support
• Drive AI-native development in line with ClassDojo’s engineering practices
• Take accountability for SLAs/SLOs and incident response across the platform surface
• Support and drive architecture and systems design decisions across infrastructure, data, DevEx, and AI tooling; ensure systems are reliable, maintainable, and operationally ready
• Partner with Security, Ops, Data, and other functions as the platform requires.

Engineering Leader – Platform Engineering

• Design, build, and operate enterprise AI platforms at scale
• Lead a global organization of 100+ engineers
• Develop, deploy, and scale AI capabilities across all businesses and functions
• Establish and embed SRE practices across AI platforms
• Implement modern Agile and product-centric engineering practices
• Build strong leadership layers and domain-aligned teams

Global Head of AI Platform Engineering – SVP

• Design, build, and operate enterprise-grade, AI-ready data platforms at scale
• Lead global engineering organization of 100+ engineers
• Partner with Data Architecture to implement target-state blueprint
• Ensure platforms enable compliant usage
• Drive automation of operations to minimize manual intervention
• Establish and embed SRE practices across all data platforms
• Implement modern Agile delivery models across platform teams
• Architect and operate cloud-native and hybrid data platforms
• Enable and operationalize enterprise data product capabilities

Global Head of Data Platform Engineering – SVP

• Assist in building, deploying, and maintaining Redis Enterprise clusters under guidance from senior engineers
• Support the operation of Redis Enterprise components including cluster nodes, quorum, master and replica shards, and proxy-based client access
• Participate in platform upgrades and maintenance, including patching, node management, and cluster health monitoring
• Work with senior engineers to deploy and configure Redis Enterprise databases
• Contribute to automation and deployment pipelines, including CI/CD workflows and Infrastructure-as-Code for Redis Enterprise provisioning
• Assist in implementing resiliency patterns, including failover, cluster recovery, and multi-node distribution
• Follow established platform governance standards, including database lifecycle management, naming conventions, and access controls
• Participate in incident response and troubleshooting
• Learn and apply best practices for Redis Enterprise architecture
• Support platform operations and compliance requirements
• Assist in monitoring and improving platform performance
• Collaborate with application teams to support common Redis use cases

Redis Platform Engineer

• Lead complex initiatives to design and deliver Confluent Kafka platforms on OpenShift (OCP), enabling scalable, resilient, and secure event streaming solutions for enterprise applications
• Design, build, deploy, and maintain Kafka infrastructure on OCP using operator-based frameworks, supporting components such as brokers, KRaft controllers, Schema Registry, Kafka Connect, Fink and Control Center
• Drive continuous improvement and modernization efforts, including platform upgrades, automation, and performance optimization across Kafka and OCP environments
• Evaluate and integrate Kafka ecosystem tools and OCP-native capabilities, ensuring alignment with enterprise architecture standards and target-state platform design
• Develop and maintain automation frameworks using CI/CD pipelines and Infrastructure-as-Code to standardize Kafka cluster provisioning, configuration, and lifecycle management
• Architect and implement high availability and disaster recovery solutions, including cross-data center deployments, replication strategies, and cluster linking for multi-region resilience
• Define and enforce platform governance standards, including naming conventions, topic management, schema governance, security policies, and data isolation strategies
• Analyze and resolve high-impact incidents, performing root cause analysis and implementing corrective actions to improve reliability and prevent recurrence
• Make key technical decisions on Kafka architecture and OCP deployment models, including cluster topology, storage integration, networking, and workload placement
• Establish and manage operational risk and control processes, ensuring compliance with enterprise security, regulatory, and audit requirements
• Optimize platform performance and cost through capacity planning, resource utilization tuning, and workload distribution strategies
• Collaborate with application teams to support onboarding, migration, and adoption of Kafka, enabling self-service capabilities and best practices
• Partner with internal platform teams (OCP, networking, security) and external vendors to drive platform delivery, resolve issues, and influence roadmap priorities

Lead Kafka Platform Engineer

• Assist in building, deploying, and maintaining Confluent Kafka platforms on OpenShift (OCP) under guidance from senior engineers
• Support the deployment and operation of Kafka components including brokers, KRaft controllers, Schema Registry, and Kafka Connect
• Participate in platform upgrade and improvement activities, including patching, configuration updates, and performance tuning
• Work with senior engineers to evaluate and integrate Kafka ecosystem tools and OCP-native capabilities
• Contribute to automation efforts, including CI/CD pipelines and Infrastructure-as-Code templates for Kafka deployments
• Assist in implementing high availability and resiliency patterns, including replication, failover configurations, and cluster linking
• Follow established platform governance and standards, including naming conventions, topic management, and security best practices
• Participate in incident response and troubleshooting, helping identify root causes and document resolutions
• Learn and apply best practices for Kafka architecture and OCP deployment models, including networking, storage, and resource allocation
• Support operational processes and controls, ensuring alignment with enterprise policies and procedures
• Assist in monitoring and improving platform performance, capacity, and resource utilization
• Collaborate with application teams to support Kafka onboarding and basic integration use cases
• Work closely with internal teams (OCP, networking, security) and vendors to resolve issues and improve platform stability.

Kafka Platform Engineer

• Define and execute the enterprise data and analytics platform strategy
• Establish standards, guardrails, and reusable capabilities for data use
• Own the strategy and lifecycle of foundational analytical data products
• Lead a unified organization spanning data engineering and analytics platforms
• Own platform-level enablement of data governance, quality, and lineage
• Lead budget, vendor strategy, and platform investment decisions
• Develop bench strength, succession pipelines, and differentiated talent
• Partner with CIOs, business leaders, and enterprise stakeholders

Vice President – Data & Platform Engineering

• Take ownership of our internal AI platform, built on the Vercel AI SDK and AWS Bedrock
• Move it into full production, build the roadmap, expand model and agent capabilities
• Integrate with internal data sources
• Evolve our Model Context Protocol server that exposes OUTFRONT's inventory, holds, and campaign tools to AI buyer agents
• Design and build the shared platform that internal automation teams use to ship agents safely
• Evaluate, integrate, and operationalize new capabilities of AWS AI stack

Lead AI Platform Engineer

• Collaborate with data platform architects to design and specify platform tooling, services, and integrations
• Build data lake and lake house architectures
• Build streaming data architectures to support data platform tooling and services
• Collaborate with software teams to integrate streaming and batch architectures with software products
• Collaborate with data scientists, analysts, and business stakeholders to MVP optimal solutions
• Collaborate with data governance teams and data platform architects to build governance into technical solutions
• Maintain systems and services that provide transparency and observability into our critical systems
• Implement and promote engineering and architectural patterns, perform code reviews, and collaborate in architectural reviews
• Collaborate with product team data engineers and architects to MVP data products, maintain data models, and promote data modeling best practice
• Provide technical leadership and mentorship to product and data engineers, guiding their growth and professional development and enabling their ability to use platform tooling and services
• Lead by example through hands-on contributions to designing, coding, and troubleshooting complex data systems
• Identify and address performance bottlenecks and optimization opportunities within data pipelines, databases, and processing frameworks
• Optimize data processing workflows to improve efficiency and reduce latency
• Lead efforts to diagnose and resolve data-related incidents in a timely manner
• Will participate in the grooming of stories
• Will be responsible for their own tasking towards the completion of stories
• Will mentor junior members of the team and guide junior members on best practice
• Will participate in design
• Will be responsible for the quality of their own code and will participate in code review of others product
• Will be responsible for integrating their own code with the team's DevOps plan and implementation

Data Platform Engineer

• 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

• Own the technical architecture of the data platform end-to-end: ingestion, storage, transformation, orchestration, serving, and observability layers.
• Author and maintain the platform architectural vision document; lead quarterly architecture reviews to assess alignment with organizational goals and technology trends.
• Define and evolve the target-state architecture for the platform, establishing a multi-year technology roadmap in partnership with engineering leadership.
• Evaluate emerging technologies, frameworks, and make evidence-based adoption recommendations.
• Serve as the final technical escalation point for complex design questions, cross-team conflicts, and build-vs-buy decisions affecting the platform.
• Lead the planning, execution, and delivery of multi-quarter platform initiatives involving multiple engineers, cross-functional dependencies, and significant organizational impact.
• Break down large, ambiguous programs into scoped workstreams; assign technical leads per workstream, define milestones, and manage cross-team dependencies and risk.
• Drive initiative kick-offs with clear problem framing, success criteria, architectural constraints, and delivery phasing — from 0-to-1 exploration through production hardening.
• Maintain stakeholder alignment throughout delivery: proactively communicate status, surface trade-offs, and escalate blockers to leadership before they become risks.
• Lead post-mortems and retrospectives for large initiatives; document and socialize lessons learned to raise the organizational bar on delivery excellence.
• Exercise functional oversight across the EDAP team: review technical designs, set quality gates, approve architectural decisions, and ensure consistency of implementation patterns.
• Define, document, and socialize platform engineering standards including coding conventions, testing requirements, CI/CD practices, schema design guidelines, and SLA frameworks.
• Establish and own the platform's technical review process (design review): triage incoming projects, chair review sessions, and ensure decisions are documented and traceable.
• Identify and drive resolution of technical debt, redundancy, and architectural drift that impede platform reliability, developer productivity, or scalability.
• Partner with security, compliance, and infrastructure teams to ensure platform systems meet governance, data privacy, and regulatory requirements by design.
• Serve as a primary technical mentor for junior and senior data platform engineers across the platform; provide structured coaching on system design, technical communication, and engineering judgment.
• Conduct and lead design reviews, architecture critiques, and technical deep-dives that strengthen the overall capability of the engineering team.
• Define what technical excellence looks like at each level of engineers and participate in calibration discussions.
• Represent the data platform in engineering-wide forums, all-hands, and external venues (conferences, open-source communities, recruiting events).
• Build a culture of documentation, reliability, and platform-as-a-product thinking across all data platform engineering functions.
• Participate in goal planning cycles as a technical voice; define engineering-led goals that improve platform reliability, developer experience, and data quality.

Lead Data Platform Engineer

• Directly manage 4-5 data platform engineers across levels (Engineer I through Senior Engineer III); serve as their primary advocate, coach, and career guide.
• Conduct regular 1:1s with each direct report focused on growth, blockers, wellbeing, and career trajectory.
• Own the career development plans for each team member; set clear expectations at every level, identify growth opportunities, and provide timely, actionable feedback.
• Drive performance management end-to-end: set goals, provide continuous feedback.
• Identify high-potential engineers and create stretch opportunities that accelerate their development toward the next level.
• Foster a team culture of psychological safety, inclusion, continuous learning, and high accountability — where engineers feel empowered to take initiative and safe to raise concerns.
• Address performance gaps early and constructively
• Own the full recruitment lifecycle for your team: define headcount needs, write and refine job descriptions, run structured interview processes, and make hiring decisions.
• Partner with recruiting to build and maintain a healthy pipeline of engineering talent at all levels; actively source candidates and represent the company at recruiting events.
• Onboard new hires effectively: ensure they have a structured ramp plan, a buddy/mentor, and clear 30/60/90-day expectations from day one.
• Own the health of the team's delivery system: sprint cadence, backlog grooming, capacity planning, dependency management, and release coordination.
• Ensure the team consistently delivers against commitments; proactively identify risks, surface blockers early, and create conditions for engineers to do their best work.
• Establish and maintain clear team-level SLAs, on-call rotations, and incident response processes; hold the team accountable to reliability standards.
• Drive quarterly planning in coordination with the Lead Engineer and Director: translate organizational priorities into team-level goals, milestones, and resourcing decisions.
• Manage cross-team dependencies with partner engineering organizations (ML, Analytics); ensure alignment and resolve conflicts at the manager level.
• Own team-level reporting and status communication to leadership: weekly/bi-weekly updates, risk flags, and delivery summaries that give stakeholders accurate, timely visibility.
• Maintain sufficient technical depth to participate in architectural discussions, evaluate engineering trade-offs, assess estimates, and identify when the team is over- or under-resourced for a given problem.
• Partner closely with the Lead Data Platform Engineer to ensure technical direction is translated into executable work; bridge the gap between architectural vision and sprint-level delivery.
• Represent technical considerations in cross-functional conversations — with infrastructure, Data Science, Analytics, and Directors — and advocate for necessary infrastructure investment.
• Contribute to technical decisions where your perspective as a manager is valuable: team ergonomics, tooling adoption, process improvements, and build-vs-buy trade-offs with organizational implications.
• Stay current with data platform trends and adjacent technologies to have informed conversations with your team and with engineering leadership about the evolving landscape.
• Serve as the primary point of contact for partner Information Systems & Services(ISS) managers, and business stakeholders on matters of team delivery, capacity, and roadmap alignment.
• Represent the Data Platform Engineering team in cross-functional planning forums, leadership reviews, and all-hands; communicate team accomplishments, challenges, and needs clearly.
• Proactively build relationships with peer managers across the organization to improve cross-team collaboration, reduce friction, and identify shared investment opportunities.
• Partner with the Executive Director on budget planning, headcount justification, and tooling procurement decisions that affect the team.
• Contribute to shaping the engineering culture and management practices across the broader engineering organization — share what works, contribute to manager forums, and help raise the bar on people leadership

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