Luis Jose Raigoso Valcarcel

Co-led a cloud cost optimization program, delivering annualized savings of approximately USD 200K through data architecture optimization, engineering practices, platform modernization, FinOps controls, and production-grade AI automation.

Led scalable data engineering solutions across batch, streaming, ETL, and ELT workflows for AI, Data Science, and BI across LATAM. Designed and optimized high-availability pipelines across diverse data sources. Managed data lakes and warehouses with partitioning, indexing, and storage optimization. Enforced data quality, auditability, lineage, security, and compliance across ingestion and transformation workflows. Led FinOps initiatives for ingestion, processing, and storage, improving cost efficiency across data operations. Orchestrated cloud-native ETL/ELT for batch and near-real-time processing. Mentored engineering teams on advanced ETL practices and code reviews to improve modularity and maintainability. Partnered with AI, BI, and Data Science teams to align platforms with analytical needs. Implemented monitoring, logging, alerting, and service metrics to improve observability and incident response. Contributed to the Data Architecture Committee and directed Data Ingest and Data Engineering teams of ~15 developers plus temporary staff.

Achievements (Leadership & Development)

Introduced and led a DevOps/DataOps culture within the team, implementing CI/CD, data versioning, monitoring, and end-to-end traceability.

Structured execution into plans and roadmaps to improve organization, prioritization, and delivery predictability.

Actively contributed as a hands-on developer in high-complexity projects, including hybrid cloud migrations, DevOps/DataOps client implementations, data governance under DAMA, and hybrid/local ETL architectures.

Core Functions (Condensed)

BI & Analytics: design and delivery of data-driven, value-oriented insights and visualizations.

Database Design & Maintenance: logical/physical modeling, performance tuning, and security.

Data Warehouse/Lake Management: partitioning, indexing, storage optimization, and cost control.

ETL/ELT & Integration: batch/stream pipelines, data quality, auditing, and lineage enforcement.

Data DevOps: CI/CD, infrastructure-as-code, automated testing, observability, and alerting.

Data Governance: standards, policies, compliance, and DAMA-aligned frameworks.

AI & Data Science Enablement: dataset provisioning, feature pipelines, and MLOps support.

Service & Project Management: SLA compliance, continuous improvement, and operational excellence.

Client Collaboration: requirements gathering, co-design, and solution adoption.

Team Leadership: coordination of technical and functional teams, task assignment, and performance supervision.