Data Analyst Jobs in Sunnyvale, CA

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Own data center OpEx and power planning, forecasting, budgeting, variance analysis, financial reporting, scenario modeling, and cost optimization. Build complex operational and financial models, develop dashboards, and provide actionable insights to executive leadership. Partner cross-functionally with data center operations, capacity planning, power and sustainability, and sales teams to support strategic decisions and business growth.

The People Data Analyst role involves analyzing employee data to provide insights on engagement and retention, developing dashboards, and ensuring data integrity for HR reporting.

Reviews and scores robotics data episodes using standardized quality rubrics. Identifies failures, unsafe behavior, edge cases, and recurring quality trends; documents findings and provides actionable feedback. Collaborates with stakeholders and teleoperators to improve review workflows, standards, training, and documentation while meeting throughput and accuracy targets.

Analyzes business and data requirements, converts them into machine learning and generative AI problems, integrates data from multiple sources, builds data models and dashboards, performs statistical analysis, develops predictive and AI models, and benchmarks model improvements. The role requires Python expertise, knowledge of data science and visualization tools, generative AI technologies, and relational and non-relational databases.

Designs and implements data warehousing, analytics, dashboards, semantic layers, and reporting solutions for supply chain planning operations. Collaborates with business, supply chain, and IT stakeholders to define requirements, document workflows, develop user stories, validate data, and improve SAP data accessibility through Databricks. Maintains data definitions and governance, tests data assets, ensures reporting accuracy, and supports self-service analytics. Requires deep supply chain experience, BI development expertise, and strong SQL, Python, and data modeling skills.

Support data center construction cost planning, forecasting, and controls across project lifecycles. Maintain program-level cost forecasts, prepare estimates and commercial risk assessments, compile benchmarking data, and coordinate with engineering and external cost consultants to optimize capital efficiency and inform location and deployment decisions.

Support D2C and B2B growth by building automated dashboards, performing funnel and conversion analyses, tracking lifecycle marketing and ad performance, and partnering with cross-functional teams to scale growth initiatives. Learn from mentors and progress into owning growth and marketing analytics domains.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Perform advanced web analytics to evaluate marketing and product performance using SiteCatalyst. Query and analyze data with SQL, present insights to stakeholders using Excel and PowerPoint, and collaborate with business partners to drive data-informed decisions. Contract role, SiteCatalyst certifications and Netezza experience preferred.

Perform data analysis, write and optimize SQL queries, derive insights, and communicate findings to stakeholders. Requires 5-7 years' experience and strong communication skills.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Lead master data governance and business analysis for product/material data across Agile PLM, SAP MDG and S/4HANA. Define governance, steward data, design MDG workflows and integrations, drive data migration/cleansing, perform impact analysis, support cross-functional stakeholders, and champion data quality improvements.

The Robot Data Operations Analyst will control robots, evaluate performance, provide quality assurance, and document observations safely in an R&D setting.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and validate data labeling outputs, provide structured feedback and hands-on training to annotators, monitor workflows and escalate issues, help apply and improve guidelines, and identify patterns in human and model outputs to maintain high-quality AI training data.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, enforce and improve guidelines, identify recurring issues and edge cases, escalate risks, and document feedback to support human-in-the-loop AI workflows.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

Audit and monitor data labeling outputs, provide actionable feedback, support onboarding and training of annotators, enforce and improve guidelines, escalate workflow issues, and document findings to maintain high-quality human-in-the-loop AI data processes.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.