Senior Analytics Engineer (AI & Business Intelligence)

Yuxi Global

1d•Remote

About The Position

Yuxi Global is seeking a Senior AI Analytics Engineer / Agentic BI Lead to support a fast-moving business intelligence team working on large-scale product and operational analytics. This role is designed for a high-performing analytics professional who combines advanced SQL expertise, enterprise-scale data experience, product-minded analytics judgment, and hands-on experience applying AI to business intelligence workflows. The successful candidate will not operate as a traditional Business Analyst or dashboard-only reporting resource. This person must be able to own ambiguous analytics problems from stakeholder discovery through SQL development, validation, AI-assisted workflow design, automation, delivery, and business adoption. The ideal candidate has experience working with large data ecosystems, preferably datasets with hundreds of millions of rows or more, and can reason deeply about data quality, performance, metric definitions, root-cause analysis, and stakeholder decision-making. This role requires someone who can work independently, ramp quickly, communicate clearly, and drive outcomes with minimal hand-holding. This is a builder-owner role. The right person can sit with business and technical stakeholders, understand the real problem, find the right data, write and optimize the SQL, validate the result, determine where AI or automation can help, build or prototype the workflow, test the output, and drive adoption. In simple terms: this person turns BI ambiguity into trusted, scalable, AI-enabled analytics capability.

Requirements

8+ years of professional experience in business intelligence, analytics engineering, data analytics, product analytics, data engineering, or related fields.
Advanced SQL expertise, including experience working with large, complex, enterprise-scale datasets.
Experience analyzing datasets at significant scale, ideally involving hundreds of millions of rows or similarly complex data environments.
Demonstrated ability to own analytics projects end-to-end, from stakeholder requirements and problem framing through implementation, validation, delivery, and adoption.
Hands-on experience applying AI, automation, or agentic workflows to analytics, reporting, data quality, or decision-support use cases.
Experience with one or more AI-enabled BI patterns such as natural language-to-SQL, BI copilots, automated insight generation, root-cause analysis, anomaly detection, data-quality monitoring, or analytics workflow automation.
Strong understanding of data validation, metric reconciliation, source-of-truth alignment, and data-quality practices.
Ability to work independently, ramp quickly, and drive outcomes without heavy day-to-day management oversight.
Strong stakeholder management skills, including the ability to clarify ambiguous requests, challenge assumptions, and guide business users toward better analytical questions.
Strong written and verbal communication skills in English.
Bachelor’s degree in Computer Science, Data Analytics, Statistics, Information Systems, Engineering, Business Analytics, Mathematics, Economics, or equivalent practical experience.

Nice To Haves

Experience supporting BI, product analytics, marketplace analytics, digital platform analytics, mobile/app ecosystem analytics, subscription analytics, developer ecosystem analytics, or large-scale consumer product analytics.
Experience with Google Cloud analytics tools such as BigQuery, GoogleSQL, Looker, LookML, Dataform, Vertex AI, or related cloud data and AI services.
Experience with BI and analytics tools such as Looker, Tableau, Power BI, Mode, Hex, Sigma, Superset, or similar platforms.
Experience with Python for analytics automation, data validation, workflow orchestration, or AI-assisted analysis.
Experience designing or evaluating text-to-SQL systems, retrieval-augmented analytics workflows, LLM-based analytics agents, or AI-assisted BI copilots.
Experience building evaluation harnesses or test approaches for AI-generated analytics outputs.
Experience with analytics engineering tools such as dbt, Airflow, Dagster, Prefect, Dataform, or similar workflow and transformation platforms.
Experience with data quality frameworks, anomaly detection, metric monitoring, or automated validation workflows.
Experience working with globally distributed teams supporting U.S. business stakeholders, preferably aligned to Pacific Time working hours.
Experience in consulting, staff augmentation, managed services, or embedded client-facing delivery environments.
Experience as a contractor or employee at Google and especially Google Play in a business intelligence role is a plus.

Responsibilities

Own end-to-end analytics initiatives from stakeholder discovery through implementation, validation, automation, and business adoption.
Partner with business, BI, data, and technical stakeholders to clarify objectives, define success metrics, and convert ambiguous requests into actionable analytics work.
Design, write, optimize, and validate advanced SQL queries against large-scale enterprise datasets.
Develop scalable analytics solutions that support operational decision-making, product insights, root-cause analysis, and recurring business reporting.
Apply AI and automation to improve BI workflows, including natural language-to-SQL, BI copilots, automated insight generation, anomaly/root-cause workflows, and data-quality monitoring.
Build, evaluate, test, and refine AI-assisted analytics workflows to ensure accuracy, explainability, repeatability, and business trust.
Identify opportunities to reduce manual reporting effort, streamline stakeholder requests, improve analytical throughput, and increase BI team productivity.
Validate analytical outputs through rigorous data-quality checks, metric reconciliation, source-of-truth review, and stakeholder confirmation.
Collaborate with BI engineers, data engineers, product analysts, and business stakeholders to align data models, metric definitions, and reporting logic.
Create clear documentation for analytics logic, SQL assumptions, data lineage, AI workflow behavior, known limitations, and stakeholder-facing outputs.
Operate independently in a fast-paced environment with limited onboarding support, while proactively identifying risks, dependencies, blockers, and decisions needed.
Communicate insights, tradeoffs, risks, and recommendations clearly to both technical and non-technical stakeholders.
Support adoption of analytics and AI-enabled BI solutions by training stakeholders, gathering feedback, measuring usage, and iterating on workflows.
Help define reusable patterns for agentic BI delivery, including evaluation methods, validation checklists, governance practices, and automation playbooks.
Mentor analysts or engineers on advanced SQL, AI-assisted analytics workflows, business framing, and end-to-end project ownership.