Principal Engineer Data Platform

Employees can work remotely

Job Description 

Team 

Join the Global Cloud Services organizations FinOps Tools team which is building ServiceNows 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 organizations 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 ServiceNows 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 ServiceNows 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 ServiceNows 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 platforms 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 ServiceNows 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 platforms 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. 

Qualifications :

To 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 AIs 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 Bachelors degree; or 12 years and a Masters 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 constraints of reserving capacity ahead of demand. 

• Multi-cloud & infrastructure: Kubernetes Infrastructure as Code (Terraform CDK CloudFormation) CI/CD and GitOps and the AWS/GCP/Azure and on-premises landscape the platform runs on. 

• Reliability & observability: SLI/SLO/error-budget design monitoring and alerting (Splunk Grafana Prometheus CloudWatch or similar) and operating data platforms in production. 

• Data contracts & quality: Fail-loud ingestion upstream contract views and correctness invariants enforced in code rather than assumed. 

• API & integration design: RESTful services authentication (OAuth/SAML) and webhook/event integrations across systems. 

Leadership & Communication 

• Conference speaking experience and recognized thought leadership in data engineering distributed systems or FinOps. 

• Proven ability to work autonomously and drive cross-team technical decisions in ambiguous greenfield environments. 

• Proven ability to lead through influence: setting technical direction and raising the bar across teams you do not manage. 

• Strong technical writing and documentation skills for both engineering- and business-facing audiences. 

• Excellent collaboration skills across engineering DevOps data product and finance stakeholders. 

• Ability to establish technical foundations for new products with long-term vision while delivering short-term results. 

Nice to Have 

• FinOps Certified Practitioner AWS/GCP/Azure architecture certifications or equivalent. 

• Open-source contributions to data engineering FinOps or distributed-systems tooling. 

• Experience with additional query and compute engines (Spark Snowflake BigQuery) and with high-performance systems languages (Rust Go C). 

• Experience with data validation frameworks (Great Expectations dbt tests etc.) and with Apache Iceberg or lakehouse architectures. 

• Patent applications or publications in data systems forecasting or cloud technologies. 

Why Join Us 

• Build and deliver high-impact software that powers financial governance and capacity planning at global scale. 

• Collaborate in a culture that values craftsmanship quality and innovation. 

• Work symbiotically with AI and automation tools that enhance engineering excellence and drive product reliability. 

• Be part of a culture that encourages innovation continuous learning and shared success. 

GCS-23

For positions in this location we offer a base pay of $221200 - $387100 plus equity (when applicable) variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline and individual total compensation will vary based on factors such as qualifications skill level competencies and work location. We also offer health plans including flexible spending accounts a 401(k) Plan with company match ESPP matching donations a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.

Additional Information :

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible remote or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation national origin age disability gender identity  veteran status or any other category protected by addition all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. 

Accommodations

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process or are unable to use this online application and need an alternative method to apply please contact for assistance. 

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations including the U.S. Export Administration Regulations (EAR) ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. 

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Remote Work :

Yes

Employment Type :

Full-time