Position SummarySamsung 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 GCP-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 GCP data platform.
- Infrastructure as Code: Manage GCP 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 GCP -- 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 GCP 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 GCP 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 GCP -- 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 GCP 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 GCP 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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Benefits @ Samsung - https://www.samsung.com/us/careers/benefits/
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.
