Senior Principal Data Engineer

UKG

1h•$184,300 - $230,000

About The Position

As a Senior Principal Data Engineer within the Enterprise Data & Analytics Office, you will lead the design, evolution, and operational excellence of Nexus, UKG’s unified, GCP-based data platform, enabling analytics, AI, and enterprise decision-making at scale. You will translate architectural vision into reliable, scalable, cost-efficient data systems, lead platform modernization efforts, and set the engineering bar for data ingestion, transformation, and operational analytics across UKG.

Requirements

15+ years of experience in data engineering, data platforms, or large-scale analytics systems.
Proven leadership delivering enterprise-scale data platforms and migrations.
Deep hands-on experience large (Petabyte) scale operating data platforms .
GCP (required): BigQuery, GCS, Cloud Composer, Pub/Sub, Dataflow, Dataform, Dataproc, Dataplex
Databricks: Delta Lake, Delta Live Tables, Spark, Unity Catalog
Strong proficiency in Python, SQL, Spark, and distributed systems
Expertise in data modeling, ELT patterns, CDC, and streaming architectures
Experience supporting AI/ML pipelines, feature stores, and analytics consumption
Strong understanding of Dimensional Modeling, ELT and modern data lakehouse patterns.
Proven ability to influence without authority across engineering, product, and architecture teams.
Strong communication skills with both technical and executive audiences.
Track record of mentoring senior engineers and leading complex initiatives.

Nice To Haves

Experience modernizing legacy warehouses and ETL platforms.
Familiarity with Data Mesh execution and domain-aligned data products.
Experience implementing FinOps practices for analytics platforms.
Exposure to AI/ML enablement, feature engineering, or agent-driven analytics.
Google Cloud certifications (Data Engineer, Architect) preferred

Responsibilities

Drive the implementation of standardized medallion architecture (Bronze–Silver–Gold) across all domains.
Establish and enforce engineering patterns for batch, streaming, CDC, and event-driven pipelines.
Drive design patterns for Fact and Dimension modeling, data mesh, and change data capture (CDC).
Design and evolve scalable data pipelines using GCP-native services (BigQuery, GCS, Composer, Pub/Sub, Dataflow) and Databricks (Delta Lake, DLT).
Ensure platform reliability, performance, and scalability for petabyte-scale analytics workloads.
Lead engineering decisions around partitioning, clustering, query optimization, and cost controls (FinOps).
Own observability standards (freshness, SLAs, error handling, monitoring).
Define and enforce engineering standards for data quality, testing, CI/CD, and production readiness.
Implement automated data quality frameworks, lineage, and metadata integration in partnership with Data Governance and Architecture teams.
Ensure all pipelines meet SOX, security, and auditability requirements.
Partner with Architects to ensure engineering implementations align to enterprise reference architectures.
Partner with Product Managers, Product Owners, Architects, and Analytics leaders to deliver high-impact data capabilities.
Act as the technical escalation point for complex production issues and platform risks.
Influence roadmap decisions by balancing technical feasibility, delivery velocity, and long-term sustainability.
Support ESE Agile delivery by enabling architecture runway and reducing technical blockers.
Mentor Principal, Senior, and Mid-level Data Engineers, raising the overall engineering maturity of the organization.
Lead by example through design reviews, code reviews, and engineering forums.
Build reusable frameworks, libraries, and accelerators that scale engineering productivity.
Foster a culture of ownership, quality, and continuous improvement.