Aldo is available for hire
Hire AldoAldo Orozco
Verified Expert in Engineering
Data Engineer and Software Developer
Zapopan, Mexico
Toptal member since October 11, 2022
Bio
Staff-level data engineer with 8+ years building and modernizing data ecosystems for big tech companies like Netflix and Etsy. Focused on the hard, high-scale problems: re-architecting orchestration, migrating thousands of tables to open table formats, and cutting the cost and latency of massive Spark workloads. I’ve worked remotely with US-based teams since 2018.
Portfolio
Netflix
Amazon EC2, Amazon EMR Studio, Kubernetes, Apache Spark, Apache Iceberg...
Etsy
Apache Airflow, Apache Spark, Big Data Architecture, Bash, Kubernetes, Helm...
CRED
SQL, Scaling, Big Data, PySpark, Kubernetes, Python, NoSQL, BigQuery...
Experience
- Python - 9 years
- Bash - 8 years
- Spark - 8 years
- Data Engineering - 8 years
- Data Warehousing - 6 years
- Amazon Web Services (AWS) - 6 years
- Apache Airflow - 5 years
- Google Cloud Platform (GCP) - 4 years
Work Experience
Senior Data Engineer
2025 - 2026
Netflix
- Built customer acquisition funnel analysis for partnership channels, informing negotiations on million-dollar contracts with strategic consumer electronics partners.
- Rebuilt core experimentation and performance-analysis tables, cutting data latency from 2-3 days to under 3 hours, accelerating go-to-market decisions across Commerce and Finance.
- Built a partnerships MCP server unifying docs, repos, Slack, and live query execution, with source citations on every answer to curb LLM hallucination. Adopted initially by a dozen key stakeholders.
Technologies: Amazon EC2, Amazon EMR Studio, Kubernetes, Apache Spark, Apache Iceberg, Apache Druid, Maestro, Agentic AI, LLM Agents, Data Modeling, Large Language Models (LLMs), LLM Integration, Artificial Intelligence (AI), Query Optimization, FinOps, Cloud, Cloud Services, Migration, AI Agent Orchestration, AI Agents, YAML, GitHub, Agentic Deployment, CI/CD Pipelines, Software Architecture, Apache Flink, Data Processing, Software Design, Design Patterns
Staff Data Engineer I
2022 - 2025
Etsy
- Re-architected Airflow on Kubernetes (GCP) through a multi-year effort with Engineering, Data Science, and Analytics Engineering, achieving zero-downtime migration of thousands of critical workloads, resilience to cloud outages.
- Authored the multi-year plan to migrate batch data to Apache Iceberg, across thousands of TB-size tables across engineering, data science, and analytics engineering teams, projected to save 100s of thousands per year in storage and compute.
- Extended an org-wide Spark job analyzer with data skew detection and input/output extraction, helping teams cut costs by an estimated 20% and laying the groundwork for job lineage.
Technologies: Apache Airflow, Apache Spark, Big Data Architecture, Bash, Kubernetes, Helm, Data Pipelines, ETL, SQL, Pipelines, Data Engineering, Google Cloud Platform (GCP), BigQuery, Google BigQuery, Google Bigtable, Google Cloud Storage, Buildkite, Jenkins, Google Cloud Build, Microservices, CI/CD Pipelines, Streaming Data, Terraform, Data Warehousing, Grafana, Prometheus, StatsD, Git, Python 3, Docker, Docker Compose, Google Compute Engine (GCE), Google Cloud SQL, Google Cloud Functions, Flask, Spark, Python, Software, PostgreSQL, Scala, Data Analysis, Databricks, ELT, Databases, Data Architecture, PySpark, Big Data, Scaling, MySQL, Data Cleaning, Data Modeling, Data Science, Web Scraping, Agentic AI, Query Optimization, Cloud, Cloud Services, Migration, Google Cloud Platform, YAML, Infrastructure as Code (IaC), GitHub, DevOps, Agentic AI Systems, API Design, Software Architecture, Data Processing, Software Design, Design Patterns
Data Engineer with experience Scaling Data - PT/FT
2022 - 2024
CRED
- Migrated dozens of data science pipelines from pandas to Spark and automated their execution, cutting runtimes from 8+ hours to under 2 hours and speeding C-level analysis of partner contracts.
- Architected a scraping and enrichment pipeline processing ~100M records to infer user age, gender, and geolocation, feeding a knowledge graph and ML models linking individuals to sports interests.
- Automated data movement across RDBMS and lakehouse engines with Airbyte (10 connectors initially), enabling self-serve downstream access for data science, product, and engineering teams.
Technologies: SQL, Scaling, Big Data, PySpark, Kubernetes, Python, NoSQL, BigQuery, Google Cloud Platform (GCP), Snowflake, Data Warehousing, Redshift, MySQL, Data Cleaning, Airbyte, Data Modeling, Data Science, Web Scraping, Artificial Intelligence (AI), Query Optimization, Cloud, Cloud Services, Google Cloud Platform, YAML, Infrastructure as Code (IaC), GitHub, DevOps, API Design, Software Architecture, Data Processing, Software Design, Design Patterns
Staff Data Engineer
2019 - 2022
Wizeline
- Re-architected a fintech’s data warehouse into a Bronze/Silver/Gold lakehouse on Delta Lake, enabling risk analysis and paving the path for streaming ingestion for loan data.
- Designed and led a data engineering bootcamp that transitioned 30+ software engineers into data roles.
- Architected an IoT cloud ingestion and monitoring solution for several million devices in collaboration with the SRE team. The project successfully kicked off, and a dozen engineers were assigned to it.
- Conducted technical kickoff sessions with customers like Etsy and a few Fintech companies, in collaboration with Architecture and Business Development, to scope multi-year data projects.
Technologies: Amazon Web Services (AWS), Google Cloud Platform (GCP), Apache Airflow, Python, Scala, Terraform, Apache Kafka, Apache Spark, Data Pipelines, ETL, SQL, Pipelines, Data Engineering, CI/CD Pipelines, ELT, Databricks, Amazon S3 (AWS S3), Amazon RDS, Amazon Athena, Amazon Kinesis, Redshift, Snowflake, Azure, Amazon EC2, AWS Lambda, CDC, Bash, BigQuery, AWS Glue, Java, Data Analysis, Spark, Kubernetes, Data Warehousing, Big Data Architecture, Software, Machine Learning, Git, PostgreSQL, Data Warehouse Design, Data Lakes, Google BigQuery, Google Cloud Storage, Google Cloud SQL, Google Compute Engine (GCE), Docker, Python 3, Jenkins, Azure Data Factory (ADF), Databases, Apache Hive, Data Architecture, PySpark, Big Data, Scaling, MySQL, Data Cleaning, Data Modeling, Data Science, Web Scraping, Artificial Intelligence (AI), Query Optimization, FinOps, Cloud, Cloud Services, Migration, Azure Databricks, YAML, Infrastructure as Code (IaC), GitHub, DevOps, API Design, Software Architecture, Data Processing, Software Design, Design Patterns
AWS Big Data Architect
2019 - 2019
Triolabs
- Implemented a service to ingest and prune brain imaging data from private laboratories in near real time using Apache Kafka and Python on AWS. The resulting data was consumed by a machine learning model in R to extract insights from the scans.
- Rearchitected a data warehouse data model to handle TB scale queries faster and speed up a drug research analysis.
- Coordinated a team of two to create aggregation pipelines on Apache Spark on the brain imaging results so that the researchers could fine-tune drug research.
Technologies: Amazon Web Services (AWS), Apache Kafka, Python, Big Data Architecture, Data Warehousing, Data Lakes, Machine Learning, Data Pipelines, ETL, SQL, Pipelines, Data Engineering, Redshift, Amazon S3 (AWS S3), Amazon EC2, AWS Batch, R, Microservices, Amazon API Gateway, Spark, Apache Airflow, Software, Java, Bash, Git, Data Warehouse Design, Scala, Apache Spark, AWS Glue, Amazon RDS, ELT, Python 3, Databases, Data Architecture, PySpark, Big Data, Scaling, Data Modeling, Databricks, Cloud, Cloud Services, YAML, GitHub, Data Processing, Software Design
Big Data Engineer
2018 - 2019
Apex Systems
- Developed over ten Spark pipelines to aggregate and store TB of data to Hive, Elasticsearch, and MongoDB for the marketing team. These aggregations were exposed via APIs, enabling the analytics team to generate campaigns to attract new users.
- Created a library for Spark jobs to efficiently enrich data sets with location data from Google Maps APIs, thus allowing for better-targeted marketing campaigns.
- Optimized Spark settings of several production workloads. Reduced cost and execution time of overnight execution to a third, minimizing report delivery delays to senior management.
Technologies: Amazon Web Services (AWS), Apache Airflow, Spark, Qubole, MongoDB, Elasticsearch, Java, Python, Spring, Data Pipelines, ETL, SQL, Pipelines, Data Engineering, Data Quality, Data Analysis, Terraform, Data Warehousing, Big Data Architecture, Software, Hadoop, Bash, Git, Google Maps API (GeoJSON), R, Apache Spark, Amazon EC2, Amazon Athena, Amazon RDS, Docker, Python 3, Databases, Apache Hive, Data Architecture, PySpark, Big Data, Scaling, MySQL, Databricks, Cloud, YAML, GitHub, Data Processing, Software Design
Embedded Software Engineer
2014 - 2018
Continental Automotive Systems
- Developed a data pipeline in Hadoop MapReduce to aggregate historical cellular network data, helping to reduce errors in a middleware service by half.
- Architected and led the development of a service to automatically re-configure a car while driving and seamlessly connect back and forth between cellular stations.
- Assisted in containerizing the development environment of people in my area, effectively helping reduce pain and setting up a stable environment.
- Gave a series of training sessions for 30 developers on unit tests and coverage using a proprietary tool. Helped remove dozens of unreachable code snippets.
Technologies: C, C++, Hadoop, Python, Bash, Git, Linux, Software, Amazon Web Services (AWS), Docker, Python 3, Jenkins, Software Design
Project History
Adaptive Big Data Pipelines
https://github.com/aldoorozco/adaptive_data_pipelinesA containerized system to generate big data pipelines developed using Apache Spark and Apache Airflow. It was automatically configured based on a data size and schema. The system provides a web page, so users with little to no experience can easily create datasets from files and specify aggregations in SQL and a cadence in which the pipeline should run. The system builds up the cloud infrastructure and submits the tailored pipelines.
Marketing Recommendation System
A series of orchestrated ETL pipelines using Apache Spark, Apache Airflow, MongoDB, and Google Geographical APIs running on AWS, which aggregate clickstream visits so that marketing campaigns could recommend users nearby weekend trips.
Brain Imaging Prediction
https://neumoratx.com/A data architecture that encompassed several components, including an ingestion pipeline to extract brain imaging data from a medical institution into a data lake, a series of pyspark and R-based pipelines to extract insight from the imaging files and refine data pipelines that merged medical records with the aforementioned insights, and finally a data warehouse where the data was stored.
Education
2018 - 2020
Master's Degree in Computer Science
ITESO, Jesuit University of Guadalajara - Guadalajara, Mexico
2010 - 2014
Bachelor's Degree in Mechatronics Engineering
Centro de Enseñanza Tecnica Industrial - Guadalajara, Mexico
Certifications
DECEMBER 2019 - DECEMBER 2021
GCP Professional Data Engineer
Google Cloud
Skills
Libraries/APIs
PySpark, Google Maps API (GeoJSON)
Tools
Apache Airflow, Git, BigQuery, GitHub, Terraform, Helm, Google Compute Engine (GCE), Amazon Athena, AWS Glue, Apache Iceberg, Qubole, Docker Compose, Jenkins, Grafana, AWS Batch, Apache Druid
Languages
Python, Bash, SQL, Python 3, YAML, C++, Java, C, Scala, Snowflake, R
Frameworks
Spark, Apache Spark, Hadoop, Spring, Flask
Paradigms
ETL, DevOps, Design Patterns, Microservices
Platforms
Linux, Docker, Databricks, Kubernetes, Google Cloud Platform (GCP), Amazon Web Services (AWS), Apache Kafka, Amazon EC2, Buildkite, Azure, AWS Lambda, Maestro, Airbyte, Apache Flink
Storage
Data Pipelines, Amazon S3 (AWS S3), Databases, Data Lakes, PostgreSQL, Google Cloud Storage, Google Cloud SQL, Apache Hive, MySQL, MongoDB, Elasticsearch, Google Bigtable, Redshift, NoSQL
Other
Data Warehousing, Big Data Architecture, Software, Pipelines, Data Engineering, Google BigQuery, Data Architecture, Scaling, Big Data, Agentic AI, Query Optimization, Cloud, Cloud Services, Migration, Google Cloud Platform, Infrastructure as Code (IaC), Data Processing, Software Design, Complex Problem Solving, Teamwork, Data Warehouse Design, Google Cloud Build, CI/CD Pipelines, ELT, Amazon RDS, Data Analysis, Data Cleaning, Data Modeling, Large Language Models (LLMs), Web Scraping, LLM Integration, Artificial Intelligence (AI), AI Agent Orchestration, AI Agents, Agentic Deployment, Agentic AI Systems, API Design, Software Architecture, Machine Learning, Streaming Data, Prometheus, StatsD, Google Cloud Functions, Amazon Kinesis, CDC, Amazon API Gateway, Data Quality, Azure Data Factory (ADF), Amazon EMR Studio, LLM Agents, Data Science, FinOps, Azure Databricks
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