Platform Engineer

• Collaborate with operations team members and Principal and Staff Engineers to understand desired system level outcomes and operational requirements.
• Investigate, design, test, deploy and support core cloud platform components, including networking, security and resilience features, logging, monitoring, and infrastructure-as-code (IaC) features, to ensure success.
• Design and maintain automated delivery pipelines (Infrastructure as Code) to ensure repeatable, secure feature deployments.
• Manage scalable cloud network topologies, including transit gateways, VPCs, subnets, security groups, and route tables, ensuring low-latency and secure communication between distributed services.
• Provide technical input and guidance regarding responsible components during planning and design sessions with Principal and Staff Engineers to ensure the success of the larger integrated system.
• Provide hands-on technical contributions to the infrastructure design and deployment with a time allocation of approximately 80% on solving technical problems and 20% mentoring, coordinating with Staff and Principal Engineers, and supporting operations.
• Proactively identify repetitive manual tasks and engineer automated solutions to prevent recurrence.
• Act as the second escalation point for complex incidents, leading thorough root cause analysis (RCA) and implementing systemic, long-term fixes.

Senior Platform Engineer, Connectivity

• Own the infrastructure, CI/CD, and developer tooling that keeps our engineering team shipping fast and safely at scale.
• Set the technical direction for reliability, security, and compliance, building these in from the ground up rather than bolting them on later.
• Shape the design and scalability of our external API, turning it into a competitive asset for customers and partners.
• Partner with engineering leadership to define roadmap priorities and translate ambiguous problems into clear technical plans.
• Mentor engineers on the team through design reviews and hands-on collaboration, raising the technical bar across the org.

Senior Software Engineer – Platform Engineering

• Own and build the ingestion layer. Design, deploy, and scale pipelines that pull from third-party APIs, internal services, and SaaS tools into BigQuery. Add new sources as the business demands.
• Own and build the transform layer. Develop and maintain our DBT project, including staging, intermediate, and marts. Maintain core business datasets: users, organizations, indexes, accounts, usage, revenue. Write tests, snapshots, and documentation. Drive data quality and trust.
• Own and build the orchestration platform. Operate the Airflow-on-Kubernetes environment that runs our ingest and DBT workloads. Improve reliability, scalability, observability, and CI/CD.
• Establish and maintain the business-context and metrics layer. Curate metric definitions and documentation that feed both human analysts and agents.
• Manage infrastructure cost and performance. Manage BigQuery, GKE, Cloud Run, and Kafka costs, right-size compute, and make sure the platform stays efficient.
• Lead and own mission-critical company-level analyses. Partner with finance, GTM, product, and exec stakeholders to answer business questions, design metrics, run experiments and evaluations, build views in BI tools, and ship dashboards that support key business decisions as well as regular reporting to the Board of Directors.
• Enable other teams to self-serve. Onboard analysts and non-DE stakeholders onto the warehouse, help them with best practices, and create reusable models and tooling.
• Set the standard for AI-assisted data workflow. Establish best AI practices and patterns that enable a small data team to operate with outsized leverage.

Senior Data 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

• Act as the primary technical interface to lead consultation and support on complex integrations leveraging Capital One APIs, File Transfers, Gateways, and integration services
• Actively support the full integration lifecycle to improve the partner experience based on user feedback
• Reduce integration friction and cycle times while safeguarding high-risk data flows in production environments
• Lead strategic projects advancing strategic mandates to expand services internationally and to new assets
• Evolve and enhance our team operations

Manager, Platform Engineer – Enterprise Product & Experience

• 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

• Serve as primary technical administrator across IRC enterprise AI environments, currently including Anthropic (Claude) and OpenAI platform deployments
• Manage user access, API key governance, workspace configurations, and environment-level settings across AI platforms
• Monitor system health, usage patterns, and API performance across AI tools; triage and resolve operational issues as they arise
• Maintain and improve observability across AI systemsTracking uptime, error rates, token consumption, and integration reliability
• Oversee and document configuration changes, environment updates, and deployment procedures across managed platforms
• Support responsible use by flagging anomalous usage patterns and coordinating with InfoSec on policy adherence and access controls
• Coordinate with the DevOps, SW Engineering and Data Engineering team(s) on deployment processes, environment access, and infrastructure dependencies required to build and maintain AI integrations
• Follow established change management procedures for all configuration changes, environment updates, and integration deployments, including documentation, testing, and appropriate approvals before pushing to production
• Develop lightweight scripts, connectors, and automations to support AI-assisted workflows across teams, primarily in Python and/or JavaScript/TypeScript
• Troubleshoot integration failures, data flow issues, and API connectivity problems across the AI ecosystem
• Collaborate with the data engineering team on AI/KM pipeline work, including vector store ingestion, retrieval configuration, and source data connections
• Contribute to technical design discussions with engineering partners, translating operational requirements into implementable solutions
• Maintain technical documentation for all integrations, including architecture notes, runbooks, and dependency maps
• Track and report on AI resource utilization across platforms, identifying opportunities to reduce waste and improve cost efficiency in coordination with the AI
• Serve as the technical point of contact with the InfoSec team on matters related to AI system security, data handling, access controls, and compliance requirements
• Support risk assessments and security reviews for new AI tools or integrations by providing accurate technical context on system behavior and data flows
• Contribute to the development of technical SOPs and best-practice guidelines for AI system use, in coordination with the AI Platform Support Director and relevant stakeholders
• Act as a technical resource for program and operations teams adopting AI tools, including answering implementation questions, supporting troubleshooting, and identifying configuration solutions
• Participate in rollout planning for new AI capabilities, providing grounded input on technical feasibility, integration requirements, and operational readiness
• Collaborate with the AI Platform Support Director on onboarding documentation and technical guidance materials for end users
• Contribute to sprint and project planning with accurate estimates on technical effort and dependencies.

AI Platform Engineer

• Provision complete infrastructure stacks for new product launches. Recent examples include full EKS buildouts (Karpenter, Istio, Crossplane), PostgreSQL/Redis data stores with IAM authentication, CloudFront distributions, and Datadog instrumentation across multiple concurrent initiatives.
• Lead Kubernetes platform evolution. Drive cluster upgrades, add-on lifecycle (Karpenter, Istio, Crossplane), pod identity migration, and namespace management across all environments.
• Design secure, scalable abstractions that hide infrastructure complexity and define the golden paths other teams build on.
• Own authentication infrastructure for internal and external products: Auth0 SSO, Azure AD/Entra, AWS IAM, EKS Pod Identity, and OIDC federation.
• Drive zero-trust infrastructure patterns. Rotating IAM credentials for RDS, per-app Istio authorization policies, and network isolation.
• Enable LLM-powered workflows (Bedrock, Claude Code, and similar) that let engineering teams move faster without sacrificing governance.

Senior Platform Engineer

• Design, build, and operate Identity & Access Management services:
• - Authentication flows, sessions, device trust.
• - Authorization and entitlements (roles, permissions, policies).
• - Security controls such as MFA and key management (working with security teams).
• Own financial operations systems:
• - Transfers and payments orchestration.
• - Ledgering and reconciliation flows.
• - Limits, risk controls, and transactional correctness.
• Build and maintain blockchain infrastructure:
• - Node and RPC management.
• - Transaction submission, monitoring, and retry logic.
• - Indexing and streaming of on-chain data into internal services and data stores.
• Define and uphold reliability and observability standards:
• - SLOs and error budgets for core services.
• - Dashboards, alerts, runbooks, and incident management practices.
• Lead large, cross-functional technical projects from design through rollout, collaborating with Product, Security, and Ops.
• Mentor and guide other engineers, contributing to engineering standards, design reviews, and documentation.
• Stay close to industry best practices in auth, key management, and secure infrastructure for crypto and trading.

• Set technical direction for the platform: drive architecture decisions, write ADRs, evaluate trade-offs, and bring the team along.
• Design, build, and operate platform services on GCP, covering compute (GKE, Cloud Run, Compute Engine), networking, IAM, secrets, observability, and CI/CD integrations.
• Author and maintain Terraform modules and a clean state strategy across multiple GCP projects and environments.
• Import, refactor, and clean up legacy infrastructure when needed.
• Build and operate GKE clusters: workload identity, networking, ingress (nginx), autoscaling, upgrades, policy, and cost controls.
• Stand up serverless and PaaS patterns for application teams (Cloud Run, Cloud Functions, Pub/Sub, Workflows) with sensible defaults baked in.
• Partner with our AI / Software Engineers to operationalize agents and models: Vertex AI Agent Engine deployments, model endpoints, eval and observability pipelines, prompt/secret management, and CI/CD for AI artifacts.
• Implement secure-by-default CI/CD with GitLab using Workload Identity Federation: no long-lived keys, least-privilege service accounts, reproducible deploys.
• Contribute to Azure and AWS work as needed: networking, identity, storage, and helping align patterns across clouds.
• Improve developer experience: golden paths, paved roads, internal docs, and templates that make the right thing the easy thing.
• Mentor other engineers on the team through code review, pairing, design feedback, and knowledge-sharing.
• Raise the technical bar across CloudOps.
• Lead incident response and post-incident reviews for systems you own and help mature how the team operates on call.

• Lead the comprehensive architecture and detailed design of enterprise platform services
• Define and implement industry-leading best practices for infrastructure as code
• Drive complex platform modernization initiatives
• Establish and enforce reliability, resiliency, and observability standards
• Partner strategically with product teams, AI engineering teams, security architects, and compliance specialists

Lead, AI Platform Engineer

• Set and drive the technical vision for the team and partner with peer staff/principal engineers to shape direction across adjacent teams (data platform, reporting, internal and external frontends & apps)
• Sit at the table with product and business stakeholders (KBRA Analytics, Bank Product) to translate strategy into a multi-quarter technical roadmap, make build/buy/sunset calls, and own the architectural decisions that come out of them.
• Own the architecture of a Snowflake lakehouse ingesting 30+ regulatory and market data sources (FFIEC, FDIC, NCUA, FactSet, Xignite, SEC EDGAR, FRED, etc.) via S3 external tables, Dask-based transforms, and SQS / Mongo change-stream event pipelines.
• Design and evolve reusable Terraform modules spanning AWS, Azure, Snowflake, MongoDB Atlas, GitLab, Datadog, etc.
• Drive CI/CD, IaC, and acceptance-test (BDD/behave) standards across the team.
• Mentor engineers and act as code-owner across IaC, data-lake, and application repos.

Lead Full-Stack Platform Engineer – KFI Data Platform

• Define, architect, and set standards for composable IaC (CDK or Terraform) patterns for Cloud Infrastructure (EKS).
• Drive the adoption and implement composable, idempotent, multi-environment GitOps workflows.
• Optimize scalability and cost-per-performance using metrics-driven automation and autoscaling technologies such as Karpenter.
• Develop and maintain observability across the platform using Prometheus, Grafana, and distributed tracing.
• Lead cross-functional efforts with application teams to define SLOs and capacity models for mission-critical services.
• Lead major production incident response efforts, drive blameless postmortems, and mentor other engineers on production incident response, postmortems, and reliability reviews.
• Create and operate resilient CI/CD pipelines for safe, rapid deployments and rollbacks.
• Champion automation, low-toil operations, and a culture of continuous improvement.
• Develop agentic workflows utilizing the team-wide context layer and operational data to accelerate development without compromising reliability.
• Act as a technical leader and mentor to elevate the team, with a strong ability to listen, evaluate, and give constructive feedback on ideas.

Staff Platform Engineer

• Drive code standards & technical architecture norms
• Own technical/engineering roadmap and architecture
• Seek and implement best engineering practices
• Participate in thorough code review to ensure quality and contribute to team momentum
• Run day-to-day operations (standups, sprint planning, retrospective, issue triage)

Platform Engineering Lead

• Lead the technical delivery of a centralized DLP platform that standardizes how data protection policies are authored, how findings are routed and scored, and how enforcement is applied across the program
• Design and build platform services using Python, AWS serverless and container infrastructure (ECS Fargate, Lambda, Step Functions, Aurora PostgreSQL, EventBridge), and infrastructure as code (CDK)
• Define interface contracts between platform components and existing production systems, ensuring clean integration boundaries
• Make architectural trade-offs that balance delivery speed, operational reliability, and regulatory defensibility
• Drive engineering decisions on schema design, API boundaries, deployment strategy, and platform lifecycle management
• Partner with DLP policy experts to translate data protection requirements into platform capabilities that produce consistent, auditable outcomes
• Establish engineering practices for the platform: CI/CD pipelines, testing strategy, deployment automation, observability
• Manage dependencies on adjacent teams and systems, unblocking delivery when external integration points stall
• Communicate platform strategy and progress to senior leadership, translating technical decisions into business impact
• Grow into people leadership as the team expands, building engineering culture and developing talent

Manager, Security Platform Engineering, DLP Control Platform

• Own the delivery of a scoped set of developer platform capabilities end-to-end (design, implementation, rollout, iteration).
• Build and evolve paved roads (golden paths, service templates, scaffolding) that make it easy to deploy, operate, and scale services.
• Run tight feedback loops with engineers across product and ML teams; measure adoption and eliminate common friction points.
• Drive improvements to GitOps workflows via ArgoCD and Terraform automation via Atlantis.
• Harden CI/CD on GitLab CI, including pipeline performance, safety, and developer ergonomics.
• Design progressive delivery patterns (canary, blue/green) as reusable platform primitives.
• Contribute to SLI/SLO frameworks and shared observability primitives (Prometheus, Grafana, Loki, Tempo, OpenTelemetry).
• Participate in the platform team's on-call rotation; reduce toil through automation and better system design.
• Improve incident response and postmortem practice so learnings translate into systemic fixes.
• Embed secure-by-default patterns into platform templates (secrets, IAM, RBAC, NetworkPolicies).
• Partner with security on compliance posture (SOC 2, ISO 27001) without slowing delivery.
• Partner with infrastructure/SRE owners on Kubernetes and core cloud foundations (networking, storage, cluster/platform primitives) where it directly improves developer workflows.
• Evolve cloud-native abstractions so the platform remains portable and resilient as our footprint grows (GCP today, with future optionality across providers).

• Lead and mentor a team of platform engineers
• Oversee the implementation and upkeep of endpoint security measures and device management systems
• Ensure optimal performance end-user experience while also providing technical guidance and mentoring to their team members
• Design, implement, and maintain platform and endpoint management solutions
• Implement endpoint security solutions to protect against malware, ransomware, and other cyber threats
• Collaborate with business stakeholders to understand their platform or endpoint requirements

Senior Manager, Global Platform Engineering

• 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

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