Program Manager, PIC Data Engineering

Nokia

. Lead architectural design and hands-on implementation of scalable cloud-based lakehouse and data warehouse solutions for PIC operations .

Posted 10/9/2026full-timeRemote • Nevada • United StatesSeniorLead💰 $125,000 - $178,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in data engineering and architecture, with a focus on building scalable cloud-based lakehouse and data warehouse solutions. Proficient in leading technical teams and implementing data governance, security protocols, and advanced analytics initiatives.

Highest-signal resume keywords

Data EngineeringCloud Data PlatformsDatabricks Lakehouse ArchitectureSQL ExpertiseData Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ArchitectureETL/ELTData ModelingDimensional ModelingData Pipeline AutomationAI-Ready Data FoundationsData Quality FrameworksCI/CD PipelinesMachine LearningAdvanced Analytics

Soft Skills

Exceptional CommunicationInterpersonal SkillsCross-Functional CollaborationMentoringLeadership

Tools & Technologies

DatabricksAWSRedshiftBigQueryUnity CatalogGenieDataOpsData Orchestration Tools

Certifications & Qualifications

Bachelor's Degree

Industry Keywords

Data GovernanceSelf-Service AnalyticsEnterprise Data SolutionsData QualityChip Fabrication Processes

Tech Stack

Tools & technologies

Amazon RedshiftAWSBigQueryCloudETLSQLUnity

About the role

Key responsibilities & impact

Lead architectural design and hands-on implementation of scalable cloud-based lakehouse and data warehouse solutions for PIC operations
Leverage Databricks and AWS or similar platforms to support Nokia's operational data needs
Drive the design, optimization, and evolution of enterprise data models and semantic layers
Engineer, build, and maintain resilient batch and real-time data pipelines using ETL/ELT and streaming solutions
Establish data quality frameworks, monitoring, and observability practices
Implement CI/CD pipelines and DataOps best practices
Lead enterprise data governance, including cataloging, lineage, metadata management, and access controls
Design and enforce security protocols and role-based access management
Build governed, AI-ready data foundations for self-service analytics, AI-assisted exploration, and advanced analysis
Enable business, engineering, and analytics teams to securely access trusted enterprise data
Develop data infrastructure for AI, automation, machine learning, and advanced analytics initiatives
Lead, mentor, and inspire product engineers while setting technical standards and promoting engineering best practices
Champion data excellence, accountability, self-service enablement, and continuous innovation

Requirements

What you’ll need

15+ years of progressive experience in data engineering, data architecture, or related fields
Proven track record delivering impactful solutions in wafer and chip fabrication processes
Bachelor's degree
5+ years of experience leading, developing, and inspiring technical teams
Deep expertise in SQL, modern cloud data platforms, and distributed data processing frameworks
Hands-on experience with Databricks, Redshift, or BigQuery
Experience building governed data platforms and data products supporting self-service analytics, AI-assisted analytics, machine learning, and advanced analytical workloads
Experience with Databricks lakehouse architecture, Unity Catalog, Genie, and Genie Code highly preferred
Strong understanding of enterprise data modeling, dimensional modeling, and semantic layers
Experience with data orchestration tools and advanced data pipeline automation
Ability to design and implement scalable, secure, governed, and reliable enterprise data solutions
Exceptional communication and interpersonal skills
Ability to partner cross-functionally with business, engineering, analytics, and technology teams

Benefits

Comp & perks

Laptop support with ordering
Remote work arrangement