Job DescriptionEmployees can work remotely
Job Description Team Join the Global Cloud Services organization's FinOps Tools team, which is building ServiceNow's next-generation analytics and financial governance platform. Our team owns the full modern data stack: Trino for distributed queries, dbt for transformations, Iceberg for lakehouse architecture, Lightdash for business intelligence, and Argo Workflows for orchestration. As the Distinguished Engineer for the FinOps Engineering Platform, you will set and own the technical vision and architecture for the entire platform, the single technical authority who unifies the data platform, the cloud development platform, the underlying multi-cloud infrastructure, and our forecasting and capacity-reservation automation into one coherent system. You will also lead the design and development of the GCS Data Warehouse, the modern lakehouse foundation that will replace and migrate the organization's existing Cloudera-based data platform, and that everything else in the FinOps Engineering Platform is built upon.
Role The FinOps Engineering Platform spans several major workstreams, each with its own Senior Staff engineers building it: the analytics and cost-governance data platform (Trino, dbt, Iceberg, Lightdash), the cloud development platform that takes analytics from notebook to production, the multi-cloud DevOps and SRE infrastructure it all runs on, the Forecast Engine that turns capacity and cost actuals into forward-looking forecasts, and the Future Capacity Reservation (FCR) automation those forecasts feed. As our Distinguished Engineer, you will lead all of it.
Underpinning all of it is the GCS Data Warehouse, and you will lead its design and development. This is the program that modernizes ServiceNow's Global Cloud Services data platform by migrating it off Cloudera (Impala, Hive, HDFS) onto the modern lakehouse (Trino, Iceberg, dbt). You will own the target architecture, the migration strategy, and the correctness bar for moving years of accumulated tables, transformations, pipelines, and consumers onto the new foundation with zero data loss, then retiring the legacy platform. Because the data platform, Forecast Engine, and FCR automation all read from and write to this warehouse, getting its architecture and migration right is the highest-leverage work on the platform.
This is a hands-on technical leadership role, not a management role. You will define the cross-cutting architecture, set the standards every workstream builds against, make the highest-leverage technical decisions, and keep the whole platform coherent as it scales. You will not manage people; you will lead through architecture, deep technical judgment, and influence, partnering closely with the Senior Staff engineers who own each workstream and with Engineering and FinOps leadership.
This is a unique opportunity to define the technical foundation and long-term direction of cloud financial management at ServiceNow's scale, and to do it at startup velocity within a Fortune 500 environment.
What You Get To Do In This Role - Own the end-to-end technical architecture of the FinOps Engineering Platform, ensuring the GCS Data Warehouse, data platform, development platform, infrastructure, Forecast Engine, and FCR automation compose into one coherent, scalable system.
- Lead the design and development of the GCS Data Warehouse and the program to migrate ServiceNow's Global Cloud Services data platform off Cloudera onto the modern lakehouse, with zero data loss and verified correctness.
- Set the technical vision and multi-year roadmap for the platform, and translate it into the concrete standards and interfaces each workstream builds against.
- Make the highest-leverage, hardest-to-reverse technical decisions: technology selection, system boundaries, data contracts, and the architectural patterns that span workstreams.
- Establish platform-wide engineering standards for reliability, determinism, observability, security, and production readiness, and hold the bar across teams.
- Lead through influence: partner with the Senior Staff engineers who own each workstream, review their designs, resolve cross-team architectural tensions, and align everyone to a single technical direction.
- Drive innovation across the platform, including the responsible use of AI/ML tooling to accelerate development and improve platform capabilities.
- Foster a culture of engineering craftsmanship, knowledge-sharing, and thoughtful quality practices across every team building on the platform.
- Move fast: keep the platform shipping in tight, high-velocity loops while protecting the architectural integrity that lets it scale.
Technical Leadership & Architecture - Define the reference architecture for the FinOps Engineering Platform and the contracts between its parts: how the data platform serves the Forecast Engine, how forecasts drive FCR automation, how the development platform productionizes analytics, and how all of it runs on the shared infrastructure.
- Lead technical decision-making on the platform-wide technology stack, system boundaries, and architectural patterns, arbitrating trade-offs that no single workstream can resolve alone.
- Establish best practices for data modeling, simulation and forecasting, pipeline development, orchestration, and platform scalability across the modern data stack.
- Own the cross-cutting non-functional requirements: reliability, determinism and reproducibility, observability, security and compliance, performance, and cost.
- Drive innovation in FinOps data analytics and forecasting, evaluating and adopting emerging technologies where they raise the platform's ceiling.
GCS Data Warehouse: Modernization & Cloudera Migration - Lead the design of the GCS Data Warehouse, the modern lakehouse foundation (Trino, Iceberg, dbt, a modern catalog) that replaces the existing Cloudera-based platform (Impala, Hive, HDFS, Hive Metastore) and serves as the substrate for the entire FinOps Engineering Platform.
- Own the migration strategy and sequencing: a phased, low-risk path that moves workloads off Cloudera incrementally rather than in a single high-risk cutover, with the legacy platform decommissioned only once each workload is verified on the new foundation.
- Establish full inventory and lineage of the existing platform first, the tables, transformations, scheduled jobs, and downstream consumers (Tableau, Lightdash, pipelines, the Forecast Engine), so nothing is migrated blind and nothing is left stranded.
- Define the data and schema translation approach: Hive/Impala schemas and partitioning onto Iceberg tables, legacy file formats onto the lakehouse, and HiveQL/Impala SQL and Spark transformations onto Trino SQL and dbt models.
- Set the correctness bar for the migration: dual-run old and new in parallel and reconcile outputs against the source platform as ground truth, with fail-loud validation so any divergence is caught before cutover, never discovered after. Petabyte-scale with zero data loss.
- Plan and execute consumer cutover and the retirement of the Cloudera cluster, capturing the infrastructure cost savings (a FinOps win the platform itself can measure) and the operational simplification of consolidating onto one modern stack.
- Navigate enterprise constraints, security, compliance, and approval processes, while keeping the migration moving at pace.
Platform Architecture Across Workstreams - GCS Data Warehouse: The foundational lakehouse the whole platform sits on, and the migration that retires the legacy Cloudera platform onto it (see above).
- Analytics & cost-governance data platform: Guide the lakehouse architecture (Trino, dbt, Iceberg, Lightdash), data modeling for cost allocation and showback, query performance at scale, and metadata, lineage, and governance.
- Cloud development platform: Guide the notebook-to-production pathways (workspace provisioning, parameterization, validation, automated deployment) so exploratory analysis reaches production safely and quickly.
- Multi-cloud infrastructure, DevOps, and SRE: Guide the Kubernetes, IaC, CI/CD, security, and observability foundation across AWS, GCP, Azure, and on-premises, and the SLO/error-budget practices that keep the platform reliable.
- Forecast Engine: Guide the deterministic, multi-period capacity and cost simulation, its accuracy and reconciliation against actuals, and its evolution into an automated, always-on forecasting service.
- Future Capacity Reservation (FCR) automation: Guide the architecture that turns forecasts into reservation recommendations, how much capacity to reserve, in which providers and regions, and by when, aligned to hyperscaler procurement lead times.
Thought Leadership & External Presence - Represent ServiceNow at industry conferences and FinOps community events.
- Contribute to open-source projects and establish ServiceNow's presence in the modern data stack and FinOps ecosystem.
- Drive technical content creation including whitepapers, blog posts, and conference presentations.
- Build strategic relationships with technology vendors and the broader FinOps community.
Collaboration & Integration - Work autonomously with guidance from Engineering and FinOps leadership, owning the platform's technical direction.
- Partner deeply with the Senior Staff engineers who own each workstream, aligning their designs to one architecture without taking the keyboard away from them.
- Collaborate with DevOps, security, and platform teams on infrastructure, CI/CD, and compliance.
- Partner with product managers, FinOps practitioners, finance, and capacity-planning stakeholders to ensure the platform serves how the business actually plans, budgets, and governs cloud spend.
QualificationsTo be successful in this role, you have - Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
- 15+ years of experience in software or data engineering, with a track record of architecting and delivering large-scale, cloud-native, data-intensive platforms with a Bachelor's degree; or 12 years and a Master's degree; or a PhD with 8 years experience in Computer Science, Engineering, or related technical field; or equivalent experience.
- Proven track record as the lead architect or top technical authority for a platform spanning multiple teams and workstreams, setting direction that others build against.
- Proven experience leading a large data platform migration or modernization, ideally off a legacy Hadoop or Cloudera stack (Impala, Hive, HDFS, Spark) onto a modern lakehouse, including the inventory, dual-run reconciliation, consumer cutover, and decommission of the old platform.
- Deep expertise across the modern data stack (Trino/Presto, dbt, Apache Iceberg, orchestration) and in distributed-systems and cloud-native architecture.
- Strong systems and backend engineering depth, with the ability to go deep in any layer of the stack to make or unblock a hard technical decision.
- Hands-on experience with cloud cost management and FinOps, including the data and economics behind capacity planning, forecasting, and reservations.
- Demonstrated ability to operate at high velocity in greenfield environments with evolving requirements, shipping production-quality systems fast without sacrificing architectural integrity.
- Strong knowledge of data structures, algorithms, object-oriented and data-oriented design, design patterns, and performance optimization.
- Deep understanding of software quality principles including reliability, determinism, observability, security, and production readiness.
- Ability to troubleshoot and reason about complex distributed systems and optimize performance and cost across the stack.
- Full professional proficiency in English.
- Comfort with development tools such as IDEs, debuggers, profilers, source control, and Unix-based systems.
Technical Expertise - Platform architecture: Designing and owning the architecture of large, multi-component platforms, including the contracts and boundaries between independently built subsystems.
- Modern data stack & lakehouse: Trino/Presto, dbt, Apache Iceberg, Lightdash, query optimization at scale, and metadata, lineage, and governance.
- Platform migration & modernization: Migrating off legacy Hadoop/Cloudera (Impala, Hive, HDFS, Hive Metastore, Spark, Oozie) onto a modern lakehouse, including schema and SQL translation, phased cutover, dual-run reconciliation against the source as ground truth, and zero-data-loss guarantees at petabyte scale.
- Forecasting & simulation: Deterministic, reproducible computation, multi-period simulation or time-series forecasting, and reconciliation of forecasts against ground-truth actuals.
- Cloud capacity & reservations: Hyperscaler capacity procurement, AWS/GCP capacity reservations (FCR), On-Demand Capacity Reservations (ODCR), and the lead-time and coordination cons
