Position SummaryHeadquartered in Plano, TX, Samsung Electronics America, Inc. (SEA) is a leader in mobile technologies, consumer electronics, home appliances and enterprise solutions. From our humble beginnings to our position today as a tech leader, our passion for innovation has been the common thread throughout our history. We've grown into one of THE most recognized global brands. We consider ourselves "relentless pioneers" that push boundaries and defy barriers. The company pushes beyond the limits of today's technology to provide groundbreaking connected experiences across its large portfolio of products and services, including mobile devices, home appliances, home entertainment, 5G networks, and digital displays. As EPA's ENERGY STAR Corporate Commitment Partner, SEA is dedicated to making a positive impact on the environment through its eco-conscious products, practices, and operations.
People | Excellence | Change | Integrity | Co-Prosperity
Samsung Electronics America is seeking a highly versatile and analytically driven AI Analytics leader to join our Data & AI Analytics organization. This is a unique, high-impact business-side role designed for a professional who can operate across the full data spectrum -- deriving sharp business insights from data, engineering the scalable infrastructure that powers those insights, and architecting the enterprise data platform that ensures everything is governed, trusted, and future-proof.
You will work within a fully Google Cloud Platform-native environment, leveraging the breadth of Google Cloud's data and AI services -- from BigQuery and Dataflow to Vertex AI and Gemini -- to deliver end-to-end data capabilities across SEA's consumer electronics, eCommerce, and B2B business lines. You will also be a hands-on contributor to SEA's growing AI and agentic development practice, building intelligent, automated workflows that amplify the value of data across the organization.
As a Senior Manager, you will directly manage a team of 3-4 analysts and/or contractors, navigate Samsung's matrixed global organization to align stakeholders across regional and HQ boundaries, and drive data and AI initiatives from ideation to business impact. You bring 8-15 years of combined experience across data analysis, data engineering, and data architecture, and you thrive where technical depth meets business strategy and people leadership.
Role and ResponsibilitiesData Analysis & Business Insights- Analytics Ownership: Design and execute end-to-end analyses on large, complex datasets to answer strategic business questions across consumer electronics, mobile, home appliances, eCommerce, and B2B segments; translate findings into clear, actionable recommendations for senior stakeholders.
- Dashboards & Reporting: Build, own, and continuously improve interactive dashboards and self-serve reporting solutions in Looker and Looker Studio; define metrics, KPIs, and business logic in alignment with stakeholder needs.
- Data Storytelling: Communicate complex analytical findings through compelling narratives and visualizations tailored to both technical and non-technical audiences including executive leadership.
- Data Quality Stewardship: Monitor, validate, and enforce data quality across analytical datasets; partner with Engineering to resolve root cause issues and establish data SLA standards.
Data Engineering & Pipeline Development- Pipeline Design & Development: Design, build, and maintain scalable batch and real-time ELT/ETL data pipelines using Google Dataflow (Apache Beam), Cloud Composer (Apache Airflow), Pub/Sub, and dbt; ensure pipelines are performant, observable, testable, and production-grade.
- BigQuery Data Modeling: Develop and maintain BigQuery datasets, tables, and data models; apply dimensional modeling, partitioning, clustering, and cost-optimization best practices to serve both analytical and operational workloads at SEA scale.
- Data Ingestion & Integration: Integrate structured and unstructured data from diverse sources -- APIs, operational databases, event streams, third-party SaaS platforms, and IoT/SmartThings device data -- into SEA's centralized Google Cloud Platform data platform.
- Infrastructure as Code: Manage Google Cloud Platform data infrastructure using Terraform; enforce IaC principles to ensure reproducibility, version control, and environment consistency across development, staging, and production.
- Observability & Reliability: Implement data quality checks, pipeline SLA monitoring, and alerting using Cloud Monitoring and dbt tests; own pipeline reliability and participate in on-call escalation for critical data flows.
Data Architecture & Governance- Enterprise Data Architecture: Design and govern the end-to-end data architecture for SEA on Google Cloud Platform -- spanning ingestion, storage, transformation, serving, and AI layers -- ensuring alignment with business strategy, scalability requirements, and global Samsung standards.
- Data Mesh & Governance: Lead the design and implementation of data mesh principles at SEA using Google Cloud Platform Dataplex -- defining data domains, establishing data product ownership, implementing federated governance, and enabling self-serve data access across business units.
- Data Catalog & Lineage: Own SEA's data catalog and metadata strategy using Google Data Catalog and Dataplex; define tagging taxonomy, lineage capture, PII classification, and business glossary standards to drive data discoverability, trust, and compliance.
- Security & Compliance Architecture: Architect and enforce data security controls across the Google Cloud Platform stack: IAM, VPC Service Controls, column-level security, dynamic data masking, and encryption; ensure architecture meets CCPA, GDPR, SOX, and Samsung global data compliance requirements.
- Architecture Standards: Define and enforce architectural standards, design patterns, and best practices for all data engineering and analytics development at SEA; conduct architecture reviews and provide technical guidance to cross-functional engineering teams.
AI, Generative AI & Agentic Development- AI-Ready Data Platform: Design the foundational architecture for AI and generative AI workloads on Google Cloud Platform -- including Vertex AI Feature Store topology, vector database design (Vertex AI Vector Search, AlloyDB pgvector), and AI data pipeline patterns that support RAG, fine-tuning, and model serving at scale.
- Agentic Workflow Development: Build and deploy AI agents and multi-agent systems using LangChain, LangGraph, and Google Agent Development Kit (ADK) that combine LLM reasoning with structured data retrieval, tool use, and automated decision-making across SEA data workflows.
- RAG Pipeline Engineering: Design and implement Retrieval-Augmented Generation (RAG) pipelines connecting BigQuery and Vertex AI Vector Search with Gemini/PaLM APIs to power intelligent internal copilots, natural language data querying, and automated insight generation.
- Generative AI Integration: Integrate Vertex AI Generative AI and Gemini APIs directly into analytics and data pipelines -- including automated anomaly summarization, stakeholder report generation, and AI-assisted data discovery capabilities.
- MLOps Support: Support Data Science teams by building feature engineering pipelines, managing data feeds to Vertex AI Feature Store, maintaining model input/output schemas, and contributing to Vertex AI Pipelines for end-to-end ML workflow automation.
- Prompt Engineering & Evaluation: Apply prompt engineering best practices; develop evaluation frameworks to assess LLM output quality, agent reliability, and RAG retrieval accuracy in production environments.
People Management & Global Stakeholder Navigation- Team Leadership: Directly manage a team of 3-4 analysts and data professionals; set clear goals and priorities, conduct regular 1:1s, provide ongoing coaching and performance development, and hold the team accountable to delivery standards and quality benchmarks.
- Contractor Management: Oversee and manage external contractors and vendor resources supporting data and AI initiatives; define scopes of work, manage deliverables and timelines, evaluate performance, and ensure contractor output meets SEA quality and security standards.
- Global Organization Navigation: Navigate Samsung's complex, matrixed global organization -- building trusted relationships with counterparts across SEA business units, Samsung Electronics HQ in Korea, and regional affiliates; effectively align stakeholders across time zones, cultures, and organizational layers to drive shared data and AI priorities.
- Influence Without Authority: Drive adoption of data-driven decision-making and AI-powered workflows across business units where you do not have direct authority; build coalitions, manage competing priorities diplomatically, and land initiatives through influence and partnership.
- Cross-functional Partnership: Act as the senior Data & AI partner for assigned SEA business lines; proactively identify opportunities to leverage data and AI to solve business problems, capture revenue, reduce cost, or improve operational efficiency -- and translate those opportunities into funded, prioritized work.
- Stakeholder Communication: Communicate data and AI strategy, progress, and outcomes clearly to audiences ranging from individual contributors to VP-level business leaders; translate technical complexity into business-relevant language and compelling narratives.
- Documentation & Knowledge Management: Author and maintain architecture decision records (ADRs), data contracts, analytical methodology documentation, and team playbooks; build a culture of documentation and institutional knowledge retention within the team.
Skills and QualificationsREQUIRED QUALIFICATIONS- Bachelor's degree in Computer Science, Data Science, Data Engineering, Information Systems, Statistics, Mathematics, or a related quantitative field.
- 8-15 years of combined hands-on experience spanning data analysis, data engineering, and data architecture in a production, cloud-native environment.
- Deep expertise in Google BigQuery -- advanced SQL, query optimization, partitioning/clustering, dataset design, cost governance, and cross-project topology.
- Proficiency in Python for data engineering, pipeline development, data manipulation, and automation scripting.
- Hands-on experience with Google Cloud Platform data pipeline services -- including at least three of: Google Dataflow, Cloud Composer (Airflow), Pub/Sub, Dataproc, Cloud Storage, or Cloud Functions.
- Strong experience with dbt (data build tool) for data transformation, modeling, testing, and analytics engineering.
- Experience with Looker and/or Looker Studio for dashboard development, semantic data modeling, and self-serve analytics.
- Demonstrated experience with Google Cloud Platform data governance tooling -- Google Dataplex, Data Catalog, or equivalent -- for metadata management, data lineage, and federated governance.
- Experience with Terraform or equivalent Infrastructure as Code tools for managing cloud data infrastructure.
- Hands-on experience integrating AI/ML APIs into data workflows -- including calling Vertex AI, Gemini, or equivalent LLM APIs as part of automated pipelines or analytical tools.
- Working knowledge of AI agent frameworks (LangChain, LangGraph, Google ADK, or CrewAI) and the ability to build or extend agentic data workflows with tool use and RAG capabilities.
- Understanding of RAG architecture -- vector embeddings, semantic retrieval, chunking strategies, and evaluation.
- Strong understanding of data modeling paradigms: relational, dimensional, and NoSQL; ability to select and apply the right model to the right problem.
- Deep knowledge of cloud data security: IAM, VPC Service Controls, column-level security, data masking, encryption, and regulatory compliance (CCPA, GDPR, SOX).
- Experience directly managing or leading a team of 2 or more analysts, engineers, or data professionals -- including setting goals, conducting performance reviews, and developing talent.
- Experience managing external contractors or vendor resources -- including scoping work, managing deliverables, and ensuring quality and compliance.
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Regular full-time employees (salaried or hourly) have access to benefits including: Medical, Dental, Vision, Life Insurance, 401(k), Employee Purchase Program, Tuition Assistance (after 6 months), Paid Time Off, Student Loan Program (after 6 months), Wellness Incentives, and many more. In addition, regular full-time employees (salaried or hourly) are eligible for MBO bonus compensation, based on company, division, and individual performance.
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