Leads the architecture and implementation of Nokia’s scalable data platforms for photonic integrated circuit operations. Responsibilities include developing lakehouse and warehouse solutions, data models, batch and streaming pipelines, data quality and observability frameworks, CI/CD and DataOps practices, governance, security, and AI-ready data foundations. The role also leads and mentors product engineering teams, enables secure self-service analytics, and drives enterprise data excellence and innovation.
At Nokia, we are shaping the future of connectivity and innovation. We are seeking a visionary and experienced Program Manager, PIC Data Engineering, to lead the development of our next-generation data platforms, driving strategic insights and enabling AI-powered solutions across our global enterprise for photonic integrated circuit (PICs) technology. This role is critical in building the robust, secure, and scalable data foundations that underpin Nokia's digital transformation and technological leadership.
Responsibilities- Strategic Data Architecture: Lead the architectural design and hands-on implementation of cutting-edge, scalable cloud-based lakehouse and data warehouse solutions in PICs operation, primarily leveraging Databricks and AWS or alike to support Nokia's evolving operation data needs.
- Data Model Evolution: Drive the design, optimization, and evolution of enterprise data models and semantic layers, ensuring they are robust, future-proof, and aligned with business objectives.
- High-Performance Data Pipelines: Engineer, build, and maintain resilient, high-performance batch and real-time data pipelines, encompassing advanced ETL/ELT and streaming solutions to deliver timely and accurate data.
- Data Trust & Quality: Establish and champion comprehensive data quality frameworks, monitoring, and observability practices to ensure the integrity, reliability, and trustworthiness of our data assets.
- Operational Excellence: Implement CI/CD pipelines and DataOps best practices, fostering automation, reliability, and secure deployment across our data ecosystem.
- Enterprise Data Governance: Spearhead the implementation of robust enterprise data governance standards, including data cataloging, lineage, metadata management, and stringent data access controls.
- Security by Design: Design and enforce strong security protocols and role-based access management across all enterprise data platforms, safeguarding sensitive information.
- AI-Ready Data Foundations: Build governed, AI-ready data foundations that empower self-service analytics, AI-assisted data exploration, and advanced analysis, leveraging capabilities such as Databricks Genie and Genie Code.
- Empowering Data Users: Enable business, engineering, and analytics teams to securely access trusted enterprise data, fostering conversational analytics and AI-assisted analysis while upholding stringent governance, security, and data quality standards.
- Innovation Catalyst: Develop the foundational data infrastructure essential for pioneering AI, automation, machine learning, and advanced analytics initiatives that drive Nokia's competitive edge.
- Team Leadership & Mentorship: Lead, mentor, and inspire a high-performing team of product engineers, setting technical standards, promoting engineering best practices, and fostering continuous professional growth.
- Culture of Excellence: Champion a culture of data excellence, accountability, self-service enablement, and continuous innovation within the organization.
- Extensive Experience: 15+ years of progressive experience in data engineering, data architecture, or related fields, with a proven track record of delivering impactful solutions in wafer and chip fabrication process.
- Education: Bachelor's degree. Master's degree preferred.
- Leadership Acumen: 5+ years of experience leading, developing, and inspiring technical teams, fostering a collaborative and high-achieving environment.
- Technical Mastery: Deep expertise in SQL, modern cloud data platforms, and distributed data processing frameworks.
- Cloud Data Platform Proficiency: Hands-on experience with leading cloud data platforms such as Databricks, Redshift, or BigQuery.
- Governed Data Platforms: Demonstrated experience in building governed data platforms and data products that effectively support self-service analytics, AI-assisted analytics, machine learning, and advanced analytical workloads.
- Databricks Ecosystem (Preferred): Experience with Databricks capabilities, including lakehouse architecture, Unity Catalog, and AI-assisted analytics features like Genie and Genie Code, is highly preferred.
- Data Modeling Expertise: Strong understanding of enterprise data modeling principles, including dimensional modeling and semantic layers.
- Orchestration & Automation: Experience with data orchestration tools and advanced data pipeline automation techniques.
- Scalability & Reliability: Proven ability to design and implement scalable, secure, governed, and reliable enterprise data solutions.
- Collaborative Communication: Exceptional communication and interpersonal skills, with a proven ability to partner cross-functionally with business, engineering, analytics, and technology teams to drive shared success.
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What you need to know about the Chicago Tech Scene
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