Principal Engineer - Data Platform

ServiceNow is a company that focuses on business reinvention through AI and data solutions. They are seeking a Principal Engineer for Data Platform Modernization to architect and lead the migration of their Global Cloud Services data platform to a modern lakehouse, ensuring data integrity and governance at scale.

Responsibilities:

• Design the GCS Data Warehouse, the modern lakehouse foundation (Trino, Iceberg, dbt, a modern catalog) that replaces the existing Cloudera-based platform and serves as the substrate for GCS data consumers
• Lead the one-year program to move GCS data off Cloudera (Impala, Hive, HDFS, Hive Metastore) so the organization can decommission it rather than renew, sequencing the work as a phased, low-risk path with each workload verified on the new foundation before the old one is retired
• Design one portable architecture that deploys independently into commercial and regulated environments, with the isolation, data-residency, access-control, and audit posture each boundary requires. Treat operating across those boundaries as first-class architecture, not a later hardening step
• Own the ingestion architecture: change data capture from the primary source systems, transactional PostgreSQL databases, landed into Iceberg. This means log-based CDC off the Postgres write-ahead log, handling upstream schema evolution, at-least-once delivery and deduplication, late and out-of-order data, and the reconciliation of streaming changes with backfills into correct, queryable Iceberg tables (merge-on-read, compaction)
• Own the streaming layer that carries those changes. Kafka is already in the estate and is the incumbent; you will assess it and decide whether to carry it forward or replace it, weighing operational weight, ecosystem fit, portability across environments, and the one-year timeline
• Define the data and schema translation approach: Hive and 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: reconcile new 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
• Design data governance and security on the lakehouse: access control, sensitive-data handling, and audit on Iceberg and Trino, including how that posture differs across commercial and regulated boundaries and how it replaces the legacy Hive and Ranger model. This is a first-class design workstream, not a footnote
• Help design the platform's operational model: the SLOs, observability, runbooks, and on-call approach that will keep it reliable in production once workloads are live
• Establish engineering standards for reliability, determinism, observability, and production readiness, and hold the bar as workloads move onto the new foundation
• Lead through influence: align the engineers building alongside you to the target architecture, review their designs, and resolve the hard technical tensions, without taking the keyboard away from them
• Navigate enterprise constraints, security, compliance, and approval processes, while keeping the program moving at pace
• Drive the responsible use of AI and ML tooling to accelerate migration, translation, and validation work
• 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
• 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
• 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

Requirements:

• Experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving
• 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 of experience in Computer Science, Engineering, or a related technical field; or equivalent experience
• Proven experience leading a large data platform migration or modernization off a legacy Hadoop or Cloudera stack (Impala, Hive, HDFS, Spark) onto a modern lakehouse, including inventory, schema and SQL translation, reconciliation against the source, cutover, and decommission of the old platform, ideally on a tight timeline
• Experience architecting for regulated or government cloud environments (such as FedRAMP baselines or DoD Impact Levels) and designing systems that deploy across separate commercial and regulated boundaries with distinct isolation, data-residency, and compliance requirements
• Deep expertise across the modern data stack (Trino/Presto, dbt, Apache Iceberg, orchestration) and in distributed-systems and cloud-native architecture
• Hands-on experience designing streaming ingestion and change data capture, ideally log-based CDC from transactional databases such as PostgreSQL into a lakehouse, including schema evolution, delivery semantics, and reconciliation of streams with backfills
• Working knowledge of streaming platforms (Apache Kafka or comparable) and the trade-offs in operating them, with the judgment to assess an incumbent and decide whether to keep or replace it under a deadline
• Proven track record as the lead architect or top technical authority for a platform or program, setting direction that others build against
• 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
• 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, data modeling, 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
• Public-cloud architecture certifications, or equivalent
• Direct experience with GovCloud-type partitions or accredited regulated cloud environments
• Experience with data validation frameworks (Great Expectations, dbt tests, or similar)
• Experience with additional query and compute engines (Spark, Snowflake, BigQuery) and with high-performance systems languages (Rust, Go, C++)
• Experience with CDC tooling such as Debezium
• Open-source contributions to data engineering or distributed-systems tooling