Daniel Chaves

Key Achievements

• Acted as primary engagement owner for global pharmaceutical accounts including Novo Nordisk and Bayer, leading global expansion across LATAM and EMEA and securing multi-year contract renewals and scope expansions.
• Scaled the portfolio from 10 to 30+ FTEs and from 1,200 to ~4,000 monthly service hours at peak, currently owning 12 concurrent enterprise engagements with a sustained CSAT of 4.7 across LATAM and EMEA.
• Delivered 10+ engagements end-to-end — legacy data migrations, modern data platforms, analytical products, and AI applications (Machine Learning, GenAI, intelligent agents, chatbots) — built on dbt, Dagster, Airflow, Snowflake, and Databricks with robust data governance frameworks.
• Served as data strategy consultant to enterprise pharmaceutical clients, planning and designing tailored data and AI solutions, advising executive stakeholders, and translating business needs into multi-quarter delivery roadmaps.
• Scoped and launched new AI service offerings — intelligent agents, ML models, GenAI, and chatbots — directly contributing to account expansion and increased client engagement.
• Designed and implemented an AI-augmented planning and execution workflow for a data migration engagement, integrating AI tools across discovery, modeling, and validation steps and accelerating delivery by 4x.
• Championed team development through mentoring, technical enablement, and career growth plans, strengthening retention and building a high-performing delivery organization.

Key Achievements

• Built multi-stage ELT pipelines (Spotify/Adventure Works) on AWS S3, DuckDB, BigQuery, and dbt, implementing Raw→Bronze→Silver→Gold medallion architectures with automated testing and documentation.
• Developed Python-based ETL pipelines for API ingestion, data validation with Pydantic, and multi-destination loading (DuckDB, S3, Motherduck), covering full data quality assurance patterns.
• Orchestrated automated batch workflows with Apache Airflow and Astronomer, and provisioned cloud infrastructure as code using Terraform for reproducible, environment-consistent data platforms.
• Implemented predictive modeling and ML pipelines with Apache Spark, processing large-scale datasets and training classification models for data-driven insights.
• Containerized data engineering applications with Docker across PostgreSQL, MySQL, DuckDB, and BigQuery, covering both OLTP and OLAP architecture patterns.

Key Achievements

• Owned the full project lifecycle as primary point of contact for sponsors and partner institutions, ensuring scope alignment, transparent reporting, and reliable delivery from kickoff to close.
• Increased average revenue per resource by 60% by redesigning data modeling and resource allocation across the portfolio, directly improving engagement economics.
• Delivered a 30% improvement in portfolio efficiency by designing and rolling out two Business Intelligence solutions that gave sponsors and leadership real-time visibility into project health and outcomes.
• Acted as Product Owner for internal data and visualization products, translating sponsor needs into prioritized roadmaps and lifting strategic alignment by 25% through market-driven prioritization.

Key Achievements

• Boosted project efficiency and delivery by 30% by implementing project management planning and scheduling methodologies.
• Collected and analyzed relevant data to inform decision-making and project strategies, improving alignment with stakeholder requirements.
• Increased project success rates by 25%, by effectively communicating with stakeholders and maintaining strong relationships.
• Skillfully steered and timely delivered over 48 technology initiatives, leveraging advanced project management strategies to ensure adherence to deadlines and quality standards.