Christiano Viana | Curriculum Vitae

About Me

Data without strategy is just storage cost. My 20+ year journey in IT has taught me that true data engineering begins with business understanding, not just code, and that data infrastructures must be invisible so that intelligence can take center stage.

Today, my work converges the technical rigor of Data Engineering with the consultative vision of Strategy and Governance. My mission is to bridge the gap between complex infrastructures and high-impact decisions, ensuring that data generates real value.

What I deliver as Consultant and Specialist:

Scalable Data Architectures: End-to-end Cloud projects (AWS, Azure, GCP), focused on Data Lakes, Data Warehouses, and Databricks.
Technical Leadership and Governance: Structuring processes for quality, security, and data democratization, raising analytical maturity.
Innovation with AI & Analytics: Implementing Machine Learning and BI solutions that transform massive data volumes into predictive intelligence.

My academic foundation in Data Science, Mathematics, and Computer Science, with a specialization in Artificial Intelligence, combined with an MBA in Business Intelligence & Analytics, allows me to move seamlessly between the depth of code and executive language.

Maturity and Creativity

I believe that technology, much like music—my great passion and hobby—requires harmony among different elements to function. I bring to projects the discipline of engineering and the creative sensitivity to solve complex problems in an innovative and collaborative way.

Thank you for the opportunity to present my professional profile. I'm open to conversations and collaborations, whether about technology or music, I'm always eager to connect!

Education

MBA in Business Intelligence, Management & Analytics
São Judas Tadeu University

Postgraduate in Artificial Intelligence
São Judas Tadeu University

Degree in Technology, Data Science
Uninove University

Bachelor's Degree in Mathematics and Computer Science
Mackenzie Presbyterian University

Professional Experience

Strategic role as Technical Reference and Consultant in one of the largest data environments in the financial sector, leading end-to-end solutions that transform complex information flows into business assets.

As guardian of the architecture, my responsibility covers the entire data lifecycle, from raw ingestion to strategic availability, ensuring scalability and governance.

Key Impact Areas:

End-to-End Architecture & Cloud: Technical leadership in building and optimizing pipelines (ETL/ELT) using Azure Databricks, Delta Live Tables, and Azure Data Lake Storage, ensuring high performance in processing large volumes (Big Data).
Data Mesh & Governance: Implementation of decentralized architecture (Data Mesh), taking ownership of domains and ensuring each data product meets strict standards of quality, security, and documentation.
Efficiency and Performance: Critical optimization of Spark/PySpark jobs and complex queries, reducing latency and computational costs in mission-critical environments.
Leadership & Mentoring: Acted as technical mentor for data analysts and scientists, disseminating software engineering standards and strengthening the data-driven culture.
Strategic Partnership (Stakeholders): Translated business requirements into high-impact data solutions, acting as the link between executive needs and technical feasibility.

Acting as a Specialist Data Consultant, delivering end-to-end solutions that cover everything from architecture design to technical implementation. Expertise in modernizing legacy structures into high-performance cloud environments, transforming data ecosystems into strategic assets across diverse business segments.

My approach combines consultative diagnosis with technical execution to ensure that companies of all sizes have robust infrastructures. The central focus is process automation, strict governance (LGPD/GDPR), and maximum financial efficiency, delivering scalable environments with optimized cloud costs.

Key Delivery Fronts:

End-to-End Data Engineering: Building robust pipelines in Python and SQL to integrate heterogeneous sources (APIs, relational/NoSQL databases, JSON, Parquet), ensuring continuous data flow from origin to consumption.
Cloud Performance and Cost Optimization: Diagnosis and restructuring of cloud environments to achieve technical efficiency and reduce operational expenses, through fine-tuning resources and refactoring complex queries.
Big Data Modeling and Architecture: Designing optimized data structures for scale, implementing engineering best practices such as version control (Git/GitHub), automated testing, and rigorous technical documentation.
Governance and Compliance (LGPD/GDPR): Implementing security layers and privacy policies, ensuring technological innovation remains fully compliant with current regulations.
Data Migration and Modernization: Technical leadership in cloud migration projects, ensuring data integrity and business continuity during technological transitions.

During this period, I took a strategic career break focused on technical and personal development.

I dedicated myself intensively to studying new technologies, especially in the fields of Machine Learning and Artificial Intelligence, through courses and complementary training.

In addition to technical skills, I experienced multicultural environments in different countries, which broadened my global perspective, strengthened my adaptability, and brought new inspiration to my professional approach.

It was also a moment of reconnection with personal values, strengthening family bonds, and developing behavioral skills such as resilience, focus, and emotional balance.

I return to the market with renewed energy, a clear purpose, and a solid knowledge base, ready to contribute with innovative solutions and act with authenticity, creativity, and results-driven focus.

Main responsibilities:

Data Pipelines (ETL/ELT): Design, develop, and automate pipelines for data collection, transformation, and loading from multiple sources (SQL, Oracle, Teradata, APIs, files, and streams) using HiveSQL and Python.
Scalable Architectures: Build and maintain Data Lakes, Data Warehouses, and distributed systems with a focus on performance, security, and governance.
Data Quality and Availability: Ensure integrity and reliability through continuous monitoring and automated validations.
Cloud and Big Data Integration: Integrate corporate data into cloud environments and Big Data tools (Apache Spark, Hadoop, Hive, Databricks).
Collaboration with Data Science and BI: Provide clean, accessible, and analysis-ready data for predictive modeling.
Documentation and Compliance: Document flows, models, and metadata according to governance and compliance standards (LGPD, GDPR).
Performance and Scalability: Enhance system performance and scalability by applying engineering best practices and process automation.

Contributions and Results Achieved:

Analytical Bases and KPIs: Creation and management of analytical bases and KPIs to support AI strategies in Telefônica's Collections area, increasing operational efficiency and accuracy of actions.
SAP-Azure Databricks Integration: Ingestion and transformation of SAP ECC data into Heineken's Azure Databricks environment, ensuring reliability and standardization.
Predictive Models: Development of Python models for customer churn prediction, achieving 85% accuracy and contributing to retention improvement and loss reduction.