The **Junior Business Intelligence Engineer** builds and supports the analytics assets that enable reliable reporting and decision-making across a software or IT organization. This role focuses on **transforming raw data into trusted datasets, metrics, and dashboards**, while adhering to established engineering patterns, data governance practices, and quality standards.
The **Junior Analytics Engineer** designs, builds, tests, and maintains curated analytics datasets (often called “models” or “data marts”) that enable trusted reporting, self-service BI, and product/business decision-making. Working under the guidance of senior analytics engineers and/or data engineers, this role converts raw and semi-structured data into well-documented, quality-checked, and stakeholder-friendly tables and metrics.
The **Distinguished Business Intelligence Engineer** is a senior-most individual contributor (IC) who defines and scales enterprise-grade business intelligence capabilities—spanning metrics, semantic layers, analytics engineering patterns, and governed self-service analytics—so leaders and teams can make fast, correct, and trusted decisions. This role anchors the “last mile” of data: transforming curated data products into reliable insights experiences (dashboards, metrics, alerts, and decision workflows) with strong performance, usability, and governance.
The **Distinguished Analytics Engineer** is a top-tier individual contributor responsible for shaping how the organization models, defines, governs, and operationalizes analytical data for decision-making and customer-facing insights. This role operates at enterprise scale, designing durable metric systems, semantic layers, and analytics data products that are trusted, observable, and cost-effective.
A Business Intelligence Engineer designs, builds, and operates the analytics layer that turns raw operational and product data into trusted, self-serve insights for decision-makers. The role sits at the intersection of data engineering, analytics, and stakeholder enablement—owning data modeling, metric definitions, dashboarding, and data quality controls that make analytics reliable and scalable.
The **Associate Data Engineer** builds and operates the foundational data pipelines, datasets, and technical enablers that allow analytics, reporting, and data products to work reliably at scale. This is an **early-career, hands-on engineering role** focused on implementing well-defined pipeline tasks, improving data quality, and learning production-grade data engineering practices under the guidance of senior engineers.
The **Associate Business Intelligence Engineer** (Associate BI Engineer) builds and maintains reliable, well-modeled analytics assets—dashboards, reports, semantic layers, and curated datasets—that help teams make timely and accurate business decisions. This role sits at the intersection of data engineering, analytics, and stakeholder enablement, translating business questions into governed, performant, self-service BI solutions.
The **Associate Analytics Engineer** builds and maintains the trusted analytical datasets that power reporting, product insights, and decision-making in a software or IT organization. This role sits between data engineering and analytics: it transforms raw, ingested data into well-modeled, documented, tested, and reusable data assets for business intelligence (BI), product analytics, and operational reporting.
The Analytics Engineer designs, builds, and maintains trusted analytics-ready datasets, semantic models, and governed metrics that power dashboards, product analytics, and decision-making across the company. This role sits between Data Engineering and Analytics/BI, translating business questions into scalable data models while enforcing quality, documentation, and consistent definitions.
The Senior FinOps Engineer is a senior individual contributor in the Cloud Economics department responsible for engineering-driven cloud financial management: turning raw usage and billing data into actionable insights, automated controls, and measurable cost efficiency outcomes. This role blends cloud platform expertise, data engineering, and stakeholder influence to ensure cloud spend is transparent, governed, forecastable, and continuously optimized without compromising reliability, security, or delivery velocity.
The **Junior FinOps Engineer** supports the Cloud Economics function by helping the organization **measure, allocate, optimize, and govern cloud spend** across teams and products. This role blends **data analysis, cloud billing mechanics, lightweight automation, and stakeholder support** to turn raw usage and billing data into actionable insights and cost controls.
A **FinOps Engineer** enables a software or IT organization to **understand, allocate, forecast, and optimize cloud spend** through a combination of engineering, data, and cross-functional operating practices. The role blends **cloud billing expertise, automation, and stakeholder enablement** to ensure teams can scale usage while maintaining cost efficiency and financial accountability.
The Lead Platform Engineer is a senior individual-contributor (IC) and technical leader responsible for designing, building, and operating a reliable internal platform that enables product engineering teams to ship software safely and quickly. This role sits in the Cloud & Platform department and blends platform architecture, DevOps/SRE practices, automation engineering, and cross-team leadership to reduce friction in software delivery while improving security, reliability, and cost efficiency.
The Storage Engineer designs, implements, and operates enterprise storage capabilities that reliably serve application, platform, and data workloads across on-premises and cloud environments. This role exists to ensure storage services meet performance, availability, scalability, security, and cost objectives—while enabling engineering teams to ship products without storage becoming a constraint or risk. The Storage Engineer creates business value by reducing downtime and incident impact, improving data protection and recovery posture, standardizing storage services, and optimizing spend through right-sizing, tiering, and automation.
A Staff Observability Engineer is a senior individual contributor in Cloud & Infrastructure responsible for designing, evolving, and operating the organization’s observability capabilities—metrics, logs, traces, profiling, alerting, and service-level measurement—so engineering teams can build and run reliable systems. The role focuses on platform-level enablement (tooling, standards, automation, and best practices) rather than owning a single service, while still participating deeply in incident response and reliability improvements for critical systems.
A **Senior Observability Engineer** designs, builds, and operates the monitoring, logging, tracing, and alerting capabilities that enable engineering teams to **detect, diagnose, and resolve production issues quickly** while meeting reliability and performance objectives. The role sits at the intersection of platform engineering, SRE/operations, and software engineering, translating system behavior into actionable signals and standards that scale across teams and services.
The Senior Kubernetes Engineer designs, builds, secures, and operates Kubernetes platforms that reliably run production workloads at scale. This role exists to provide a standardized, automated, and supportable container orchestration foundation—so application teams can ship faster while meeting enterprise expectations for availability, security, cost, and compliance.
The **Principal Monitoring Engineer** is the technical authority responsible for designing, standardizing, and continuously improving the organization’s monitoring and observability capabilities across cloud infrastructure, platforms, and production services. This role ensures that engineering teams can detect, diagnose, and resolve issues quickly through high-quality telemetry (metrics, logs, traces, events) and reliable alerting, aligned to customer-impacting outcomes and SLOs.
The **Lead Observability Engineer** designs, implements, and governs the observability capabilities that enable reliable, secure, and high-performing cloud services at scale. This role ensures engineering teams can detect, understand, and resolve production issues quickly by building standardized telemetry (metrics, logs, traces, profiling) and turning it into actionable insights (SLOs, dashboards, alerts, incident context).
The **Junior Storage Engineer** is an early-career infrastructure engineer responsible for provisioning, operating, and supporting enterprise storage services across on-prem and/or cloud environments. The role focuses on reliable day-to-day execution—handling service requests, participating in incident response, monitoring capacity/performance, and maintaining runbooks and automation under guidance of senior engineers.
A **Junior Kubernetes Engineer** supports the day-to-day operation, reliability, and continuous improvement of Kubernetes clusters and the platform components that run on them. The role focuses on executing well-defined tasks—cluster hygiene, workload onboarding, troubleshooting, and automation—under the guidance of senior platform engineers, SREs, or a Kubernetes/Platform Engineering lead.
The **Associate Network Engineer** is an early-career individual contributor in the **Cloud & Infrastructure** department responsible for supporting, operating, and improving the organization’s network services under the guidance of senior network engineers. The role focuses on reliable day-to-day network operations (LAN/WAN/Wi-Fi/VPN), incident and request fulfillment, standardized changes, and disciplined documentation that enables scale and consistency.
The **Staff NLP Engineer** is a senior individual contributor (IC) responsible for designing, building, and operationalizing natural language processing (NLP) and large language model (LLM) capabilities that power customer-facing product experiences and internal intelligence workflows. This role owns the technical approach for complex language problems—such as search relevance, summarization, conversational interfaces, classification, and retrieval-augmented generation (RAG)—and ensures solutions meet enterprise standards for reliability, privacy, and cost.
The **Staff Generative AI Engineer** is a senior individual contributor who designs, builds, and operationalizes generative AI (GenAI) capabilities—typically LLM-powered products, platform services, and internal developer enablement—at enterprise production standards. This role bridges advanced ML/LLM engineering with software architecture, reliability, security, and responsible AI governance to deliver scalable, measurable business value rather than isolated demos.
The Staff AI Evaluation Engineer designs, builds, and operationalizes the evaluation systems that determine whether AI models and AI-powered product features are *good enough to ship* and *safe enough to scale*. This role creates the measurement “truth” for AI quality by defining metrics, building test suites and automated evaluation pipelines, running human and automated grading programs, and connecting offline results to online product outcomes.
The **Senior RAG Engineer** designs, builds, and operates **retrieval-augmented generation (RAG)** systems that connect large language models (LLMs) to enterprise knowledge and product data—safely, reliably, and cost-effectively. The role exists to move LLM use cases from prototypes to **production-grade AI capabilities** with measurable quality (groundedness, relevance, accuracy), robust governance, and operational excellence.
The Senior MLOps Engineer designs, builds, and operates the systems and processes that reliably deliver machine learning models into production and keep them healthy over time. This role bridges ML development and production-grade engineering by creating automated, secure, observable, and cost-efficient pipelines for training, deployment, monitoring, and governance of models.
The Senior LLM Engineer designs, builds, evaluates, and operates Large Language Model (LLM) capabilities that power user-facing product features and internal automation across a software or IT organization. This role turns ambiguous business needs (e.g., “make support faster,” “improve content quality,” “extract insights from documents”) into reliable, secure, cost-effective LLM systems that can be shipped and maintained in production.
The Senior AI Evaluation Engineer designs, implements, and operationalizes robust evaluation systems to measure the quality, safety, reliability, and business performance of AI models—especially modern ML and LLM-based capabilities—throughout the development lifecycle and in production. The role translates ambiguous “model quality” questions into measurable metrics, repeatable test suites, and automated gates that prevent regressions and enable responsible scaling of AI features.
The **Senior AI Agent Engineer** designs, builds, and operates **LLM-powered agents** that can plan, use tools, retrieve enterprise knowledge, and complete multi-step tasks reliably in production. The role sits at the intersection of software engineering, applied ML, product integration, and operational excellence—turning foundation models into **safe, observable, cost-controlled, and measurable** user-facing capabilities.