• Assist in data analysis and the creation of data visualizations to support business decision-making. • Perform large data crunching and analysis to identify trends and patterns. • Develop and maintain SQL reports to provide insights to various departments. • Collaborate with team members to understand data requirements and deliver solutions. • Participate in team meetings and contribute to project discussions. • Document processes and methodologies for future reference.

• Architect for RAG: You’ll design and scale the pipelines for Retrieval-Augmented Generation (RAG), transforming massive volumes of unstructured IT logs and documentation into optimized Vector Embeddings. • Scale vector infrastructure: You will be responsible for the health and performance of our vector databases (e.g., Pinecone, Milvus, or Weaviate), ensuring sub-second retrieval speeds for agentic reasoning loops. • Engineer semantic layers: Move beyond simple ETL to build knowledge graphs and semantic layers that provide agents with the necessary context to navigate complex infrastructure puzzles. • Automate data excellence: Using a keen eye for detail, you’ll build automated data guardrails to detect noise, bias, or PII (Personally Identifiable Information) before it reaches the model, ensuring our AI remains safe and impactful. • Solve meaningful challenges: Serve as the bridge between raw, messy data sources and deep technical AI work, identifying and resolving quality issues at the source. • Progress to production: With a well-defined methodology and software engineering prowess, you will build, deploy, and maintain the CI/CD pipelines for our data infrastructure, ensuring that our context window remains fresh and reliable.

• Ensure standardization, organization and reliability of the company's information • Enforce data usage, access and control policies • Ensure the quality, consistency and currency of critical data • Identify and correct inconsistencies, duplicates and errors • Map and document data sources • Structure and organize databases/data repositories • Perform analyses to support strategic and operational decision-making • Develop and maintain data models • Promote a data-driven culture

• Own analytics and AI-enabled workflow workstreams, from problem framing and stakeholder alignment to solution design, insight generation, adoption, and measurable business impact • Partner closely with Finance stakeholders to understand planning, forecasting and reporting workflows, and identify opportunities to improve them through data, automation, and AI • Translate Finance and business challenges into structured analytical frameworks, including data modeling, KPI design, forecasting, experimentation, and decision-support workflows • Design and build scalable data products, including pipelines, tables, dashboards, reporting layers, and AI-ready datasets that enable automated, flexible, and action-oriented Finance analytics • Build and test prototypes for AI-enabled Finance workflows, such as automated business review narratives, anomaly detection, variance explanation, forecast drivers, natural-language insight discovery, and self-service reporting • Partner with Data Engineering, Finance Systems and central data teams to move prototypes into reliable, governed, production-ready solutions • Contribute to defining and standardizing KPI frameworks for financial metrics ensuring alignment across Finance and business teams • Design dashboards, reporting solutions, and self-service analytics experiences that improve adoption, reduce manual reporting, and help business partners make faster, more consistent decisions • Analyze business and financial metrics to uncover key drivers of growth, retention, churn, productivity, and efficiency, and recommend clear data-backed actions • Contribute to scalable, repeatable analytics frameworks and best practices that improve Finance decision-making and create consistency across planning, forecasting, reporting, and business reviews • Apply strong governance, quality control, and documentation practices to ensure AI-enabled workflows are accurate, explainable, secure, and appropriate for Finance decision support • Communicate insights, recommendations, and roadmap tradeoffs to stakeholders and leadership through clear storytelling, executive-ready presentations, and concise written narratives