Luis Negrete

Stellaris Project (engagement with SpacexAI)

• Evaluated AI model outputs across Python, SQL, PySpark, and data engineering tasks, assessing reasoning quality, code correctness, execution results, and debugging approaches.
• Designed and reviewed technical task scenarios for AI model training and benchmarking, including analytics and distributed data-processing use cases.
• Applied hands-on data

Amgen Sensing Project (engagement with ZS Associates)

• Designed reusable PySpark data models and data engineering frameworks in Databricks, supporting global manufacturing analytics, historical ingestion, and KPI reporting across multiple sites.
• Integrated APIs, AWS S3, Smartsheet, and enterprise data sources into automated pipelines, enabling trusted datasets for downstream analytics and stak

Master in Data Analytics at the University of Niagara Falls, Canada. Focused on advanced data engineering, machine learning, and business analytics projects.

• Modernized legacy Oracle ETL workflows into Python, PySpark, Scala, and Airflow pipelines, reducing reporting-process time by 75% while improving scalability and maintainability.
• Designed advanced SQL/PLSQL transformations, Spark SQL workflows, dimensional data models, and curated datasets supporting Data Science, BI, customer analytics, eligibility, upsell, and targeting use cases.
• Research

• Built and maintained official data-warehouse inputs for a customer payment-scoring predictive model, increasing available model records by more than 50% versus the prior approach.
• Designed SQL/PLSQL transformations, curated datasets, and analytical data models supporting customer scoring, seller eligibility, upsell eligibility, targeting, BI, and commercial decision-making.
• Partnered with Da

• Developed Power BI and Qlik Sense dashboards, KPI scorecards, and reusable datasets using SQL/PLSQL and Python for commercial, operational, and customer-intelligence teams.
• Delivered an executive customer-portability analytics solution end to end, from validating official data sources and gathering stakeholder requirements to defining KPIs, reconciling business formulas, designing visualizati

• Supported database migration and process automation initiatives and developed a database-backed inventory-tracking interface, reducing reliance on manual spreadsheet workflows.
• Contributed to an RFID-based inventory pilot, improving operational visibility and reducing manual tracking.
• Supported digitalization initiatives by translating operational needs into practical data and technology sol

• Developed reusable KPI datasets and Elastic Stack reporting with monitoring dashboards and alerts for internal/external banking applications across the country.
• Analyzed application and service logs and applied unstructured data modeling techniques to support operational monitoring in a financial-services environment.
• Produced technical documentation and analytical support to improve knowled

• Modernized SQL workflows into Microsoft SSIS/SSAS pipelines, reducing reporting-process time by 40% while improving data quality and delivery speed.
• Contributed to a data-warehouse project supporting performance reporting and decision-making across sales channels.
• Supported analytics and reporting processes for customer-intelligence stakeholders, helping improve access to reliable commercial

Graduated with Distinction. Graduate studies focused on data engineering, machine learning, business analytics, and applied data solutions. Selected capstone: “Machine Learning Trading Decision System for SPY Using Technical, Macroeconomic, and Sentiment Signals.”

Issued Aug 2026

Issued Aug 2026 · Expires Aug 2027

Postgraduate specialization focused on business intelligence, analytics, data modeling, reporting, and data-driven decision-making.