Carlos Samuel Guerrero

Architecting the migration of a legacy SQL Server ODS/DW, fed by 5 ERP sources (~5.3B rows, 2.6 TB, plus an ~88 TB data lake), to Microsoft Fabric.

• Designed a 4-layer medallion platform (Bronze, Silver, Gold, Consume) across 16 isolated workspaces (Dev, Int, UAT, Prod), with folder-scoped Git integration and a dedicated service principal per environment.
• Architected a metadata-driven ingestion framework: one control schema drives an orchestrator and reusable pipeline templates that route by source type (on-prem SQL Server via gateway, ADLS Gen2 CSV, Azure SQL DB). New tables onboard through configuration, not code.
• Implemented two change-capture strategies: watermark-based incremental loads, and full-load row-hash change detection on Delta Change Data Feed for sources without reliable change columns.
• Built a custom Fabric CI/CD engine in Python on the Fabric REST API and Azure DevOps YAML, with cross-workspace dependency resolution, drift detection, a reconcile mode, and retry with backoff.
• Automated environment management: ephemeral feature-branch workspaces, bootstrap scripts, Variable Library value sets per environment, schedule promotion, and versioned Warehouse SQL migrations.
• Defined platform standards: an 8-column lineage contract, an immutable Bronze layer, naming and RBAC policy, and pull request and peer-review guidelines.

Key Achievements:

• Landed 16 ERP tables, including a 26M-row general ledger, with exact source-to-Bronze row-count parity verified automatically after every run.
• Put 260+ automated tests (including local PySpark/Delta tests) in front of every pull request, alongside SQL linting, config validation, and secret scanning.
• Delivered the Bronze ingestion layer and full CI/CD foundation in about 6 months, using AI-assisted engineering (Claude Code with Azure DevOps integration).

• Architected end-to-end data platforms on Microsoft Fabric using OneLake and Lakehouse patterns
• Designed Medallion architectures supporting both relational and graph-based analytics
• Led migration initiatives from SSIS to Fabric-native pipelines and notebooks
• Built secure ingestion frameworks using managed identities and Key Vault integration
• Designed graph data models enabling customer 360, fraud detection, and relationship analytics
• Partnered with security and governance teams to implement RBAC, RLS, and audit logging
• Mentored junior and mid-level engineers on cloud-native data engineering practices

Key Achievements:

• Established documentation standards for data engineering processes, improving knowledge sharing across departments.
• Reduced ETL runtimes by 60% through Delta optimization and ELT patterns
• Standardized graph modeling framework reused across multiple domains
• Successfully delivered Fabric-based platforms supporting billions of records

Lead a team of 4, comprising 1 DBA, 1 Power BI developer, 1 Data Analyst, and 1 Data Engineer, into the adoption of Microsoft Fabric as the Enterprise Unified Data Solution.

• Collaborated with cross-functional teams (Revenue, Admissions & Discharges, Claims) to define requirements and develop end-to-end solutions using Azure Databricks, Azure Data Factory, Azure Key Vaults, and Azure Functions.
• Mentoring Power BI developers, Junior Data Engineers, and Data Analysts on how to leverage Power Query and Power BI to generate insightful reports.
• Modeling Data using Star schema techniques to load the data into the Data Warehouse.
• Leveraged advanced analytics tools to create interactive dashboards that provided actionable insights into key business metrics.
• Ensured data quality through rigorous testing, validation, and monitoring of all data assets, minimizing inaccuracies and inconsistencies.
• Optimized data pipelines by implementing advanced ETL processes and streamlining data flow.
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
• Delivered exceptional results under tight deadlines, consistently prioritizing tasks effectively to meet project timelines without compromising quality or accuracy.

Implementing Data Governance in Microsoft Fabric through:

• Identity & Access control using Microsoft Azure Entra ID to authenticate users and service principals to Fabric.
• Metadata Management through Microsoft Azure Purview. Scanning Fabric items, extracting metadata to provide a searchable data catalog, and enforcing policies like data labeling.
• Applying Semantic Model Security using Row-level security (RLS) implemented in semantic models to control data visibility for different users.

Lead the transition effort of the current BI Solution from Databricks to Microsoft Fabric. Resulting in a 15% savings in OPEX.

• Reengineered existing ETL workflows to improve performance by identifying bottlenecks and optimizing code accordingly.
• Migration and re-engineering of all Sparks notebooks and Sparks jobs in Microsoft Fabric.
• Setting up and testing the security framework in Azure through Service Identity, allowing Microsoft Fabric to access the storage account securely.
• Contributed significantly towards setting up an automated monitoring mechanism using state-of-the-art technologies and tools, which led to proactive issue identification and resolution.
• Mentored junior team members in best practices for software development, code optimization, and troubleshooting techniques.
• Optimized data pipelines by implementing advanced ETL processes and streamlining data flow.
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
• Over 2 years of experience developing Epic Caboodle Clinical access, quality, and Financial reports using SQL, Power BI, Tableau, SSIS, and SSRS.
• Modeling Data using Star schema techniques to load the data into the Data Warehouse.

Implementing Data Governance in Microsoft Fabric through:

• Identity & Access control using Microsoft Azure Entra ID to authenticate users and service principals to Fabric.
• Metadata Management through Microsoft Azure Purview. Scanning Fabric items, extracting metadata to provide a searchable data catalog, and enforcing policies like data labeling.
• Applying Semantic Model Security using Row-level security (RLS) implemented in semantic models to control data visibility for different users.

• Designed robust database architecture that supported seamless integration of new datasets and facilitated rapid analysis capabilities.
• Modeling Data using Star schema techniques to load the data into the Data Warehouse.
• Analyzed large datasets to identify trends and patterns in customer behaviors.
• Ensured data quality through rigorous testing, validation, and monitoring of all data assets, minimizing inaccuracies and inconsistencies.
• Reengineered existing ETL workflows to improve performance by identifying bottlenecks and optimizing code accordingly.
• Optimized data pipelines by implementing advanced ETL processes and streamlining data flow.
• Implemented cutting-edge machine learning algorithms to unlock valuable insights from large volumes of structured and unstructured data.
• Participated in strategic planning sessions with stakeholders to assess business needs related to data engineering initiatives.
• Delivered exceptional results under tight deadlines, consistently prioritizing tasks effectively to meet project timelines without compromising quality or accuracy.
• Over 2 years of experience in Epic Cogito Analytics (Caboodle and Epic-BI integration) across Access, Clinical, and Revenue Cycle operation areas.
• Over 2 years of experience developing Epic Caboodle Clinical access, quality, and Financial reports using SQL, Power BI, Tableau, SSIS, and SSRS.

• Fine-tuned query performance and optimized database structures for faster, more accurate data retrieval and reporting.
• Modeling Data using Star schema techniques.
• Enhanced data quality by performing thorough cleaning, validation, and transformation tasks.
• Streamlined complex workflows by breaking them down into manageable components for easier implementation and maintenance.
• Optimized data processing by implementing efficient ETL pipelines and streamlining database design.
• Designed scalable and maintainable data models to support business intelligence initiatives and reporting needs.
• Led end-to-end implementation of multiple high-impact projects from requirements gathering through deployment and post-launch support stages.
• Evaluated various tools, technologies, and best practices for potential adoption in the company''s data engineering processes.
• Collaborated with cross-functional teams for seamless integration of data sources into the company''s data ecosystem.
• Established robust monitoring processes to detect system anomalies.
• Conducted extensive troubleshooting to identify root causes of issues and implement effective resolutions in a timely manner.
• Extract data from various sources (databases, APIs, flat files) and clean, transform, and load it into
  Power BI using Power Query.
  • Design data models within Power BI, creating relationships between tables to facilitate analysis and
  reporting.
  • Utilize DAX (Data Analysis Expressions) to create calculated columns and measures that provide deeper
  insights into the data.

Implementing Data Governance in Microsoft Fabric through:

• Identity & Access control using Microsoft Azure Entra ID to authenticate users and service principals to Fabric.
• Metadata Management through Microsoft Azure Purview. Scanning Fabric items, extracting metadata to provide a searchable data catalog, and enforcing policies like data labeling.
• Applying Semantic Model Security using Row-level security (RLS) implemented in semantic models to control data visibility for different users.

• Automated routine tasks using Python scripts, increasing team productivity and reducing manual errors.
• Modeling Data using Star schema techniques to load the data into the Data Warehouse.
• Documented and communicated database schemas using accepted notations.
• Designed data models for complex analysis needs.
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
• Built and maintained ETL pipelines in Google Cloud Environment.
• Support Data Scientist with automation process using BigQuery and ML models.

• Designing & building data architectures, including the design & set-up of scalable (big) data lakes (full cloud or hybrid cloud), scheduled transformations & data governance for the Microsoft Azure SMB team.
• Working hand-in-hand with the data scientist, deploying machine learning as scalable API services in a perfect fit with the data architecture of the Microsoft SMB team.
• Assisting the project team in deploying data applications, including, if needed, close-to-real-time architectures for the Microsoft Azure SMB team.
• Work in a team with clients and have a responsibility for managing project timelines, status meetings, and client coaching & training.
• Administered cloud-based data storage solutions, ensuring scalability and security of sensitive information.
• Fine-tuned query performance and optimized database structures for faster, more accurate data retrieval and reporting.

Overall responsibilities:
• Architect, develop, and deploy (both hybrid cloud & full cloud) data architectures in the Microsoft Azure SMB team.
• Act as a data engineer expert on the latest trends in the data analytics landscape and share best practices with the internal team.
• Leverage Open Source technology in building scalable and best-practice set-ups.
• Inspire customers with insights & opportunities on new data-driven use-cases.
• Work with external vendors and partner technologies, including (but not limited to) Azure
• Embrace project management aimed at getting the job done. This will include working with client data engineers and data scientists, but also business stakeholders
• Be an on- and offline ambassador for the Microsoft SMB team.

• Spearheaded initiatives to improve team performance, such as regular code reviews, knowledge sharing sessions, and targeted training programs.
• Modeling Data using Star schema techniques to load the data into the Data Warehouse.
• Increased efficiency of the development process by standardizing coding conventions, templates, and reusable components within the team''s toolkit.
• Improved data warehouse performance by optimizing queries and implementing indexing strategies.
• Conducted thorough code reviews to ensure adherence to industry standards and maintain high-quality deliverables.
• Designed and developed ETL processes for efficient data extraction, transformation, and loading.
• Reduced processing times by utilizing parallel processing techniques in ETL operations.
• Enhanced data quality and accuracy through meticulous testing and validation procedures.
• Optimized storage capacity utilization by implementing effective partitioning strategies for large datasets.

• 4.3 GPA
• Ranked in Top 3% of class