Sr Principal Data Engineer at UKG

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

18 Apr, 26

Salary

0.0

Posted On

18 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, GCP, BigQuery, Data Modeling, Python, SQL, Spark, Streaming Architectures, Data Governance, CI/CD, Mentorship, Observability, Analytics, Data Pipelines, Leadership, Collaboration

Industry

Software Development

Description

Why UKG: At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That’s what we do. We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you’ll get flexibility that’s real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters—and so do you. As a Senior Principal Data Engineer (P6) 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. Key Responsibilities- Enterprise Data Engineering Leadership: 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). Platform & Pipeline Excellence: 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). Engineering Standards, Quality & Governance: 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. Cross-Functional Delivery & Influence: 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. Mentorship & Organizational Impact: 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. Qualifications- Experience 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. Technical Expertise- 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. Leadership & Collaboration 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. Preferred Qualifications- 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 Company Overview: UKG is the Workforce Operating Platform that puts workforce understanding to work. With the world's largest collection of workforce insights, and people-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent, is unmatched. It's this expertise that equips our customers with the intelligence to solve any challenge in any industry — because great organizations know their workforce is their competitive edge. Learn more at ukg.com. UKG is proud to be an equal opportunity employer and is committed to promoting diversity and inclusion in the workplace, including the recruitment process. Disability Accommodation in the Application and Interview Process For individuals with disabilities that need additional assistance at any point in the application and interview process, please email UKGCareers@ukg.com

Responsibilities

Lead the design and operational excellence of UKG’s GCP-based data platform, Nexus. Drive the implementation of standardized architecture and ensure platform reliability and performance for analytics workloads.