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Hi, I'm Enmanuel Cuadros

Senior Data Engineer

I diagnose messy data problems, trace them to the source and prove each fix with before-and-after results.

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Enmanuel Cuadros — Senior Data Engineer, professional headshot

Lima, Peru · Remote work with US teams and US time zones

Live architecture

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AI-Powered Data Lake: Sources (CRM, ERP, APIs & events) → Bronze → Silver (S3 Bronze, Glue + PySpark, Quality checks) → Gold (dbt on Athena, KPIs & reports, CloudFormation) → AI layer (Claude Sonnet, Claude Opus, MCP server)Sources
CRMHubSpot, Salesforce
ERPSAP exports
APIs & eventsWhatsApp, webBronze → Silver
S3 Bronzeraw, as received
Glue + PySparkclean, dedupe, UTC
Quality checksschema + rulesGold
dbt on Athenadaily metrics
KPIs & reportsrevenue, growth
CloudFormationS3, Glue, AthenaAI layer
Claude Sonnetprofiling, NL → SQL
Claude Opusbusiness insights
MCP serveragents query lake

Measured results

rows compared row by row across a 70-pipeline migration
172.9M
rows compared row by row across a 70-pipeline migration
of affected records recovered after an identifier change broke matching
96–97%
of affected records recovered after an identifier change broke matching
audience segments synced weekly to an ad platform API, with no duplicates
51
audience segments synced weekly to an ad platform API, with no duplicates
years diagnosing and fixing data problems
~6
years diagnosing and fixing data problems
01

table-diff: Prove a Data Migration Is Right

Compares 1,000,000 rows in 0.09s on a laptop and finds exactly the 3 planted bugs; exit codes make it a CI gate.

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Data Incident Casebook

Match rate restored from 57% to 98%, a 15% revenue overstatement closed, and 5 reusable guard checks.

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AI-Powered Data Lake: Claude + MCP Server

End-to-end pipeline from raw data to business-ready KPIs, where AI agents query the lakehouse directly via MCP

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Technical Expertise

Data quality, the modern data stack, and LLM extraction validated with evals

Programming & ML Stack

Python
Primary language for data engineering, ML, and ETL pipelines
SQL
Advanced querying, optimization, and database design
Scala
Functional programming for Spark applications
PySpark
Distributed data processing at scale
Pandas
Data manipulation and analysis
NumPy
Numerical computing and array operations
scikit-learn
Machine learning algorithms and model training
TensorFlow
Deep learning and neural networks
PyTorch
Research-focused deep learning framework
Jupyter
Interactive development and data exploration

Data Engineering & Big Data

Apache Spark
Distributed computing for big data processing
Apache Airflow
Workflow orchestration and scheduling
Kafka
Real-time streaming data pipelines
dbt
Data transformation and analytics engineering
Databricks
Lakehouse platform for scalable ETL & ML pipelines
BigQuery
Serverless data warehouse for analytics
Snowflake
Cloud data platform for data warehousing
Delta Lake
ACID transactions for data lakes
MLflow
ML lifecycle management and experiment tracking

Cloud & DevOps

Google Cloud
BigQuery, Cloud Storage, Cloud Functions, Composer
Microsoft Azure
Data Factory, Data Lake, Synapse, ML, Fabric
AWS
S3, EMR, Lambda, Glue, Redshift
Docker
Containerization for consistent deployments
Kubernetes
Container orchestration at scale
Git
Version control and collaborative development
CI/CD
Automated testing and deployment pipelines

BI & Analytics

Power BI
Interactive dashboards and business intelligence
Tableau
Data visualization and analytics
Looker
Modern business intelligence platform