MELISSA VILLALTA

• Developed and maintained scalable data pipelines using Databricks for distributed data processing, supporting large-scale transformations and integrations across Sales and Finance datasets.
• Designed and implemented dimensional data models and modular DBT (Data Build Tool) transformations to create reliable, reusable datasets for analytics and reporting.
• Used GitHub for version control and collaborative development.
• Delivered actionable insights via Tableau dashboards, powered by curated data layers built in Databricks and Snowflake.
• Engaged with cross-functional stakeholders to gather requirements, define data specifications, and deliver trusted datasets aligned with business objectives.

Key Achievements:

• Architected a BI framework for the Chief Revenue Officer (CRO) organization, integrating siloed Sales and Finance repositories to enable unified, data-driven decision-making.
• Reengineered and migrated data flows that had relied on manual spreadsheet-based calculations (for over 5 years), by implementing automated SQL queries and data ingestion pipelines using Salesforce as the primary data source - improving accuracy, scalability, and operational efficiency.

• Partnered with business teams to analyze operational data and identify opportunities to streamline processes, improve SLA performance, and reduce incident resolution time.
• Extracted and transformed data using IBM Cognos and Python, developed clean and scalable data models, and created visualizations in Tableau to support data-driven decision-making.
• Conducted on-site visits to Monterrey, Mexico to discuss SLA performance with stakeholders, gather service feedback, and align on improvement opportunities.

Key Achievements:

• Built a machine learning-based Ticket Text Classifier to categorize free-text incident descriptions, significantly reducing manual triage and decreasing ticket volume.
• Led the design and implementation of a Root Cause Analysis (RCA) framework, contributing to a 39% reduction in SLA/KPI breaches by improving issue traceability and resolution time.
• Automated repetitive workflows using Python and Excel scripting, eliminating manual overhead and saving the equivalent of 3 FTEs in operational effort.
• Reduced incident backlog aging from 3.14 to 1.50 days, enhancing overall support efficiency and customer satisfaction.

• Applied Six Sigma and Lean methodologies to develop and implement performance metrics and statistical techniques for inspecting, analyzing, and resolving quality issues across key processes.
• Led continuous improvement initiatives, driving operational excellence and process optimization through structured problem-solving and waste reduction strategies.
• Supported and guided cross-functional teams by providing leadership, strategic direction, and a clear vision aligned with business objectives and quality standards.
• Promoted a data-centric culture by embedding root cause analysis and measurable KPIs into ongoing quality and process improvement efforts.

Key Achievements:

• Achieved a measurable reduction in customer complaints and enhanced customer satisfaction by executing formal improvement plans focused on the most impactful service and quality issues.