• Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment. • Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications. • Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking. • Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques. • Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable. • Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies. • Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection. • Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics. • Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment. • Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments. • Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions. • Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders. • Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

• Maintain and evolve corporate data environments • Develop, maintain and optimize ETL (Extract, Transform, Load) processes • Perform database maintenance and tuning to ensure performance, availability and data integrity • Administer and support SAS environments, including tasks related to SAS DBA and SAS DI Studio (SIS) • Support legacy applications and processes, proposing continuous improvements • Monitor, analyze and address failures in data load and integration processes • Handle a significant volume of tickets, meeting established SLAs and ensuring operational continuity • Assist in identifying and resolving incidents related to the data environment • Produce and maintain technical documentation for processes and procedures • Participate in continuous improvement, automation and data architecture modernization initiatives • Support projects related to the implementation and evolution of the Data Lake

• Design and maintain enterprise reporting architecture supporting program management, operational reporting, and executive dashboards. • Develop scalable data models, reporting frameworks, and business intelligence solutions using industry best practices. • Collaborate with business stakeholders to define reporting requirements, key performance indicators (KPIs), and data visualization strategies. • Design and optimize SQL queries, stored procedures, and data pipelines supporting enterprise reporting initiatives. • Develop executive dashboards and interactive reports using Power BI, Tableau, or similar business intelligence platforms. • Ensure reporting accuracy through data validation, quality assurance, and data governance processes. • Partner with Data Analysts, Business Analysts, and technical teams to identify new reporting opportunities and improve data accessibility. • Maintain technical documentation, data dictionaries, reporting standards, and architectural diagrams. • Support executive governance meetings by providing reporting solutions and analytical insights that inform strategic decision-making. • Recommend continuous improvements to reporting architecture, data integration processes, and business intelligence capabilities.

• Lead the data dimension of the migration for the SAP SQL Anywhere layer (~3.5M rows; 8,946 triggers; 805 stored procedures; 40k+ access points) • Evaluate and deploy a change data capture mechanism to enable coexistence stitching (no managed CDC for SQL Anywhere; dbtran is not CDC) • Ensure single-writer ownership during the dual-run • Protect data integrity and performance of the fiscal ledger system • Maintain the data side of the equivalence harness (production history replay) • Produce masked/synthetic datasets in compliance with Brazil's LGPD • Define the migration and reconciliation plan in waves, avoiding split-brain • Extract and document embedded business logic as reusable artifacts