Data Strategy and Artificial Intelligence

Data ingestion through batch and streaming methods using tools like Apache NiFi, Kafka, Azure Data Factory, and AWS Glue; managing data lakes, warehouses, and lakehouses with platforms such as Snowflake, BigQuery, Databricks, and Azure Synapse; implementing dimensional modeling; and orchestrating ELT/ETL pipelines with tools like Airflow, dbt, and Prefect.

Cloud-native architecture for data (Data Mesh, Data Lakehouse); unified platform on AWS, Azure, or GCP: infrastructure as code for data environments; data security and compliance (GDPR, DGA, HIPAA, etc.).

Self-service reporting (Power BI, Tableau, Looker); operational and strategic dashboards; report governance; KPIs and democratization of data access.

Supervised and unsupervised machine learning; deep learning, NLP, computer vision; predictive and prescriptive models applied to demand forecasting and supply chain, anomaly/fraud detection, customer segmentation and scoring, and pricing optimization; MLOps (MLFlow, Vertex AI, Azure ML, SageMaker) and generative AI based on LLMs (copilots, chatbots, content automation).

Advanced statistical and mathematical models; simulation and optimization (Monte Carlo, linear programming); data science applied to specific domains (industry, healthcare, finance, etc.) and custom algorithms for business rules.