Maria Travasso

Freelance Data Engineer - Upwork - Remote

(2025-04 - 2026-02)

Designed scalable ETL/ELT pipelines using Python, dbt, and Snowflake, reducing processing time by 40–60% for multiple clients.
Orchestrated automated workflows with Airflow and Jenkins CI/CD in AWS, ensuring reliable, version-controlled deployments.
Built high-volume ingestion from REST APIs and JSON sources into cloud warehouses, improving data freshness and availability.
Optimized Snowflake performance via clustering and tuning, cutting compute costs by 30–50% while maintaining data quality.

Data Engineer - Wolfspeed - Durham, NC

(2024-01 - 2025-03)

Built dbt models with Jinja/Python, streamlining analytics workflows and accelerating stakeholder access to insights.
Orchestrated Airflow pipelines with automated validation, reducing data latency by 70% and enabling near real-time decision-making.
Designed optimized tables/views for analytics, increasing query speed by 45% and empowering faster business decisions.
Implemented automated data quality checks, improving accuracy by 30% and significantly reducing downstream errors.
Mentored junior engineers and led design reviews, driving best-practice adoption and elevating team performance.
Integrated REST APIs into Snowflake, reducing manual preparation by 40% and ensuring real-time data availability.

Data Engineer - Acara Solutions - Cary, NC

(2021-04 - 2023-11)

Optimized ELT pipelines in Talend, increasing processing efficiency by 50% and delivering faster data to downstream analytics users.
Architected staging/transformation/reporting layers, improving query performance by 40% and reducing long-term maintenance costs.
Integrated dbt Cloud, Airflow, and Talend into CI/CD pipelines, enabling reliable deployments with zero downtime impact.
Tuned clustering/performance, decreasing query times by 35% and improving responsiveness for business users.
Established data quality frameworks, ensuring production reliability and minimizing data-related incidents.
Collaborated cross-functionally to streamline ETL workflows, accelerating end-to-end processing and team productivity.

Data Engineer - HCL America - United States

(2019-03 - 2020-11)

Engineered automated ETL pipelines for large-scale data, reducing manual effort by 50% and load time by 65%, delivering major operational time savings.
Optimized Snowflake architecture (warehouses/clustering/storage), enhancing query performance by 45% while significantly lowering compute costs.
Deployed quality checks and validation, cutting production data errors by 60% and building greater trust in data assets.
Ensured accurate/timely data delivery to analytics teams, directly enabling faster and more informed business decisions.
Built API integrations for vendor/SaaS ingestion, reducing retrieval time by 35% and improving data freshness for reporting.
Developed Azure Databricks-based Spark pipelines to process high-volume datasets, improving ETL efficiency by 45% and enabling scalable handling.
Integrated Azure Blob Storage with Databricks for secure ingestion and multi-agent processing, reducing storage costs by 35% and supporting LLM-ready data prep.

Technical Business Analyst / SQL Developer - Multimata LLC - United States

(2013-06 - 2019-02)

Gathered requirements via stakeholder interviews and workshops, aligning solutions with state guidelines and business priorities to drive accurate project outcomes.
Collaborated with cross-functional teams to gather requirements and design data structures/databases in SQL Server, enabling scalable data foundations for growing business needs.
Developed stored procedures to strengthen data integrity/validation, reducing inconsistencies and improving system reliability for mission-critical reporting.
Handled JSON data extraction/loading into SQL Server, ensuring accurate transformation/integration and seamless data flow across legacy and modern systems.
Optimized SQL Server for databases and T-SQL procedures, accelerating ETL processes and reporting speed, resulting in faster insights for business users.
Leveraged Azure Databricks for data processing and pipeline prototyping, accelerating development cycles and improving transformation accuracy by 40%.
Utilized Azure Blob Storage with Databricks for secure data staging and multi-agent orchestration, enabling efficient handling of semi-structured sources.

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