EN:
I’m a Data Engineering Analyst specialized in Azure Databricks, Microsoft Fabric, and SQL Server, with a strong background in data platform architecture, lakehouse design, and ETL/ELT engineering.
My career started in database administration, where I learned performance tuning, reliability, migrations, and troubleshooting — skills that today give me a deep understanding of how data platforms behave under real workloads.

Over the years, I’ve delivered solutions across renewable energy, logistics, manufacturing, sustainability, and gas transportation, integrating data from APIs, ERPs (SAP, V360, RM, Protheus), RPA (UiPath), and cloud services.
I enjoy building platforms that are scalable, governed, and reliable, enabling teams to make better decisions with trustworthy data.

Key project highlights:

• Designed a lakehouse DW for renewable energy (FP&N, contracts, warranties, harvest analytics) using SQL Server, SSIS, and Fabric.
• Modernized Azure + Databricks pipelines for manufacturing, improving stability and reducing reprocessing.
• Implemented ADLS Gen2 governance + Fabric workspaces for logistics dashboards (supplier excellence, SLA).
• Built Python pipelines to extract UiPath Orchestrator data (Queues, Jobs, Triggers) into SQL Server.
• Created Fabric pipelines and PySpark notebooks to collect Fabric admin metrics and ingest Historian API data for gas transportation analytics.
• Participated in SQL Server upgrades (2008→2017/2019) and Oracle 12c migrations (Azure→OCI).

I’m passionate about building data systems that are fast, clean, and reliable — and about continuously learning new ways to improve them.

PT‑BR:
Sou Analista de Engenharia de Dados especializado em Azure Databricks, Microsoft Fabric e SQL Server, com forte atuação em arquitetura de plataformas de dados, lakehouse, pipelines ETL/ELT e engenharia de confiabilidade.
Minha carreira começou em administração de bancos de dados, onde aprendi tuning, continuidade, migrações e troubleshooting — base que hoje me permite entender profundamente o comportamento de plataformas de dados em produção.

Entreguei soluções nos setores de energia renovável, logística, manufatura, sustentabilidade e transporte de gás, integrando dados de APIs, ERPs (SAP, V360, RM, Protheus), RPA (UiPath) e serviços em nuvem.
Gosto de construir plataformas escaláveis, governadas e confiáveis, que permitam decisões melhores com dados de qualidade.

Destaques de projetos:

• Arquitetura lakehouse DW para energia renovável (FP&N, contratos, garantias, safra) usando SQL Server, SSIS e Fabric.
• Modernização de pipelines Azure + Databricks em manufatura, aumentando estabilidade e reduzindo retrabalho.
• Governança ADLS Gen2 + workspaces Fabric para dashboards logísticos (excelência de fornecedores, SLA).
• Pipelines Python para extrair dados do UiPath Orchestrator (Queues, Jobs, Triggers) para SQL Server.
• Pipelines Fabric e notebooks PySpark para coletar métricas administrativas do Fabric e ingerir dados da API Historian.
• Participação em upgrades de SQL Server (2008→2017/2019) e migrações Oracle 12c (Azure→OCI).

Sou apaixonado por construir sistemas de dados rápidos, limpos e confiáveis — e por aprender continuamente novas formas de aprimorá‑los.

2024 – Now | Smarthis
Data Engineering Analyst
Azure • Databricks • Fabric • Lakehouse • Pipelines • Metadata‑Driven Frameworks

2022 – 2024 | Smarthis
Junior Business Intelligence Analyst
Python • PySpark • SSIS • T‑SQL • Dimensional Modeling • SCD2

2021 – 2022 | Power Tuning
Junior DBA
SQL Server • Performance Tuning • Shell Script • ERP (Protheus)

2021 | Orion Systems Engineering
DBA Trainee
SQL Server Upgrades • Oracle 12c Migration (Azure→OCI) • ERP (RM, Protheus)

2020 – 2021 | Orion Systems Engineering
DBA Intern
SQL Server • Oracle • MySQL • PostgreSQL • Monitoring • Backups • Deployments

2018 – 2020 | AEJE (NGO)
IT Intern
Support • Infrastructure • Computing Education

2016 – 2017 | Contax S/A
Junior Attendant

2014 – 2015 | Carioca Engenharia S/A
IT Young Apprentice

• Delta Lake • Delta Live Tables (DLT)
• Lakehouse architecture
• LakeFlow • DABs (Databricks Asset Bundles)
• PySpark for ingestion, transformation, and optimization
• Metadata‑driven frameworks
• Performance tuning & troubleshooting

• Azure Data Factory (ADF)
• ADLS Gen2 (governance, ingestion zones, security)
• Azure SQL Database & SQL Server
• Key Vault • VNet • Blob Storage
• CI/CD for data pipelines (manual + framework‑based)

• Lakehouse & Warehouse
• Pipelines • Notebooks (PySpark)
• Dataflows Gen2 • OneLake
• Power BI semantic models

• Python (requests, SQLAlchemy, automation)
• PySpark (structured transformations)
• SQL / T‑SQL (DW modeling, SCD2, incremental loads)
• SSIS (legacy ETL modernization)
• Shell Script (Linux automation)

EN: Designed a lakehouse‑style DW with SCD2, incremental loads, and governed ingestion for FP&N, contracts, warranties, and harvest analytics.
PT‑BR: Arquitetura lakehouse com SCD2, cargas incrementais e ingestão governada para FP&N, contratos, garantias e análises de safra.

EN: Implemented ADLS Gen2 governance, Fabric workspaces, and ingestion automation for supplier performance analytics.
PT‑BR: Governança ADLS Gen2, workspaces Fabric e automação de ingestão para dashboards de excelência e SLA.

EN: Supported ingestion, transformation, and metadata‑driven frameworks (DLT, LakeFlow, DABs) for SIOP and SC&E data.
PT‑BR: Suporte a ingestão, transformação e frameworks orientados a metadados (DLT, LakeFlow, DABs) para dados de SIOP e SC&E.

EN: Built Python pipelines to extract Orchestrator data (Queues, Jobs, Triggers) into SQL Server for enterprise analytics.
PT‑BR: Pipelines Python para extrair dados do Orchestrator (Queues, Jobs, Triggers) para SQL Server.

EN: Built Fabric pipelines and PySpark notebooks to collect admin activity events and ingest Historian API data.
PT‑BR: Pipelines Fabric e notebooks PySpark para coletar eventos administrativos e ingerir dados da API Historian.

EN: Azure (ADF, ADLS Gen2, Azure SQL, Blob Storage, Key Vault, VNet), Microsoft Fabric (Lakehouse, Warehouse, Pipelines, Dataflows Gen2), Databricks (Delta Lake, DLT, LakeFlow, DABs).
PT‑BR: Azure (ADF, ADLS Gen2, Azure SQL, Blob Storage, Key Vault, VNet), Microsoft Fabric (Lakehouse, Warehouse, Pipelines, Dataflows Gen2), Databricks (Delta Lake, DLT, LakeFlow, DABs).

EN:

• Lakehouse & Data Warehouse design
• ETL/ELT pipelines (ADF, SSIS, Fabric Pipelines, PySpark, T‑SQL, Python)
• Dimensional modeling (Star Schema, SCD2, incremental loads)
• Metadata‑driven frameworks (DLT, LakeFlow, DABs)
• Data governance, ingestion zones, and reliability engineering

PT‑BR:

• Arquitetura Lakehouse e Data Warehouse
• Pipelines ETL/ELT (ADF, SSIS, Fabric Pipelines, PySpark, T‑SQL, Python)
• Modelagem dimensional (Star Schema, SCD2, cargas incrementais)
• Frameworks orientados a metadados (DLT, LakeFlow, DABs)
• Governança, zonas de ingestão e engenharia de confiabilidade

EN: SQL Server (on‑prem & cloud), Oracle, MySQL, PostgreSQL, Delta Lake, OneLake.
PT‑BR: SQL Server (on‑prem e cloud), Oracle, MySQL, PostgreSQL, Delta Lake, OneLake.

Specialties / Especialidades:

• Performance tuning
• Query optimization
• Migrations (SQL Server 2008→2017/2019, Oracle 12c Azure→OCI)
• Backup/restore, monitoring, continuity
• Troubleshooting complex workloads

EN: Python (requests, SQLAlchemy, automation), PySpark, SQL, T‑SQL, Shell Script (Linux), PowerShell.
PT‑BR: Python (requests, SQLAlchemy, automação), PySpark, SQL, T‑SQL, Shell Script (Linux), PowerShell.

EN: UiPath Orchestrator (Queues, Jobs, Triggers), API ingestion, ERP data pipelines (SAP, V360, RM Corpore, Protheus).
PT‑BR: UiPath Orchestrator (Queues, Jobs, Triggers), ingestão de APIs, pipelines de dados de ERP (SAP, V360, RM Corpore, Protheus).

EN: Power BI (semantic models, DAX basics), Dataflows Gen2, Fabric Warehouse.
PT‑BR: Power BI (modelos semânticos, DAX básico), Dataflows Gen2, Fabric Warehouse.

• Email: fabricioasn95015@outlook.com
• LinkedIn: https://linkedin.com/in/fabricioalmeida2
• GitHub: https://github.com/fabricioasn

EN: I love building data platforms that make analytics faster, cleaner, and more reliable.
PT‑BR: Gosto de construir plataformas de dados que tornam as análises mais rápidas, limpas e confiáveis.