Nelson Gomes de Oliveira Junior

I am committed to innovation and the pursuit of greatness. I am looking for a challenging role where I can offer my experience, mentor others, and continue to learn from dynamic teams.

• 2013- Current – VOLVO GROUP
✓ Position: Senior Data Engineer

Main responsibilities:

- Data Pipeline Development:
✓ Designed and implemented end-to-end data pipelines using Azure Data Factory.
✓ Orchestrated data workflows, including data extraction, transformation, and loading (ETL) processes.
✓ Collaborated with cross-functional teams to ensure seamless data flow across systems.
- Databricks and Azure Platform Expertise:
✓ Leveraged Databricks for big data processing, analytics, and machine learning.
✓ Utilized Azure services such as Azure Blob Storage, Azure SQL Database, and Azure Data Lake Storage for data storage and retrieval.
✓ Managed Azure Clusters for distributed data processing.
✓ Data Modeling and Schema Design:
✓ Developed logical and physical data models for efficient data representation.
✓ Created entity-relationship diagrams (ERDs) and schema designs to support business requirements.
✓ Ensured data consistency, integrity, and scalability.
- Pyspark, Scala, Pandas:
✓ Wrote Pyspark, Scala and Pandas scripts for data transformation, aggregation, and feature engineering.
✓ Implemented custom user-defined functions (UDFs) in Pyspark for specialized data processing.
✓ Optimized code for performance and scalability.
- Data Ingestion Framework:
✓ Designed and implemented data ingestion strategies for real-time and batch data.
✓ Integrated data from various sources (APIs, databases, logs) into a unified data lake.
✓ Ensured data quality and reliability during ingestion.
- Performance Tuning and Optimization:
✓ Profiled and optimized data processing jobs to minimize execution time.
✓ Tuned Spark configurations, partitioning, and caching for optimal performance.
✓ Monitored resource utilization and addressed bottlenecks.
- Collaboration and Documentation:
✓ Worked closely with data analysts and business stakeholders to understand requirements.
✓ Documented data lineage, transformations, and dependencies.
✓ Conducted knowledge sharing sessions within the team.
- Stable Team: Data Engineer Role
✓ Collaborated closely with product owners and designers to refine user stories.
✓ Participated in daily stand-ups, sprint planning, and retrospectives.
✓ Conducted pair programming sessions to enhance knowledge sharing.
✓ Assisted teammates during peak workloads, ensuring smooth progress.

• 2010-2013 – HSBC Bank
✓ Position: Credit Specialist Sr

Main responsibilities:
- As a key member of the data science team, I played a pivotal role in ensuring the success of machine learning (ML) initiatives in Credit Risk.
- Data Preparation for ML Models by meticulously transforming raw data into a specific format suitable for training ML models, involving techniques as data cleaning, feature engineering, and ensuring data consistency.
- Collaborating closely with other data engineers, ensuring that the data pipeline was robust and efficient, allowing seamless integration with ML algorithms.
- For production data provision, focused on sourcing and delivering production data to feed ML models in real-world scenarios.
- This involved understanding business requirements, coordinating with cross-functional teams, and ensuring timely data availability for model deployment.
- My work contributed directly to the team’s ability to derive actionable insights and drive data-driven decision-making. Through my efforts, we optimized ML model performance and enhanced organizational outcomes.
 

• 2005-2010 – SPAIPA IND. BRAS. DE BEBIDAS S/A. (Coke franchise)

✓ Position: Sr System Analyst
• 2013-2013
✓ Position: BI Coordinator

Main responsabilities:
- Collect information toward the company (Sales, Logistics, Market Research, Material Control, Finance) for building IT requirements, and create a BI platform to attend many business areas.
- Data modeling for meeting business requirements by utilizing the best methodologies (multi-dimensional data model, snow flake, hybrid model, structured, semi-structured, and unstructured data)
- Focused on Data Marts and Data Warehouses creation for reaching the business needs with data integrity across multiple businesses rules and products.
- Data integration between the Oracle staging area and SAP (MM, FI, HR) in order to extract information and feed the Business Intelligence system in order to attend many data marts.
- Continuously monitoring the ETL process in order to improve performance and approaches for storage optimization.
- Committed on developing the best code, fully documented, to attend the BI area, using the best practices on coding perspective. Bringing the knowledge to the team and providing the training needed for the team achieve the same results.

✓ Machine Learning Crash Course with TensorFlow APIs provided by Google
✓ Advanced Data Engineering with Databricks
✓ Azure DevOps Services Fundamentals
✓ Docker and Kubernetes: The Big Picture
✓ DevOps Foundations: Continuous Integration and Continuous Delivery
✓ Continuous Delivery and DevOps with Azure DevOps: Managing Builds
✓ Continuous Delivery and DevOps with Azure DevOps: Release Pipelines
✓ Designing and Implementing Microsoft DevOps Solutions
✓ DevOps Foundations: Planning and Implementing a DevOps Strategy
✓ SAS Programming I
✓ SAS Programming II
✓ SAS Macro Language
✓ SAS Management Console
✓ SAS Information Map Studio
✓ SAS Web Report Studio
✓ SAS OLAP Cube Studio
✓ SAS Delivery Portal
✓ SAS Administration Fast Track
✓ IBM Cognos Report e Transformation