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Aldo Orozco
Verified Expert in Engineering
Data Engineer and Software Developer
Zapopan, Mexico
Toptal member since October 11, 2022
Bio
Aldo has over 12 years of experience as a software engineer, with eight years focused on data solutions. His broad expertise in embedded Linux systems, cloud infrastructure, site reliability engineering (SRE), and high-performance data architectures gives him the upper hand in dealing with complex systems. Throughout his career, Aldo has assumed multiple roles, including those of a developer, consultant, architect, and lead.
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
- Bash - 8 years
- Python - 7 years
- Data Warehousing - 4 years
- Apache Airflow - 4 years
- Spark - 4 years
- Amazon Web Services (AWS) - 3 years
- Terraform - 3 years
- Google Cloud Platform (GCP) - 3 years
Preferred Environment
Apache Airflow, Spark, Python, Terraform, Kubernetes, Google Cloud Platform (GCP), Amazon Web Services (AWS), Helm, Data Warehousing, Big Data Architecture
The most amazing...
...things I've done include a huge Apache Airflow re-platforming on Kubernetes and Spark pipeline optimizations, which reduced execution by 90%.
Work Experience
Senior Data Engineer
2025 - PRESENT
Netflix
- Migrated old pipelines to new monorepo and optimized jobs.
- Migrated old pipelines to new monorepo and optimized jobs.
- Migrated old pipelines to new monorepo and optimized jobs.
Technologies: Amazon EC2, Amazon EMR Studio, Kubernetes, Apache Spark, Apache Iceberg, Apache Druid
Staff Data Engineer I
2022 - 2025
Etsy
- Led an Airflow migration from version 1 to 2 and rearchitected the platform from simple VMs to modern open source helm charts, while modernizing 100s of DAGs and implemented validations and guardrails for platform users.
- Rearchitected a SQL parsing service and introduced multiprocessing, effectively reducing the processing time from 30 to five minutes.
- Implemented skew inference and input/output collection services on Spark jobs executed across the company, allowing users to optimize their pipelines.
- Implemented several RESTful microservices on Python to manage ad-hoc testing of the Airflow environment.
- Coordinated and kickstarted the migration of kubeflow pipelines to airflow.
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, PHP, Databricks, ELT, Databases, Data Architecture, PySpark, Big Data, Scaling, MySQL, Data Cleaning
Data Engineer with experience Scaling Data - PT/FT
2022 - 2024
CRED
- Rearchitected and migrated 20+ pandas pipelines running on cron-based VMs to Spark and Airflow, while also pruning deprecated logic.
- Migrated a handful of image processing pipelines to Spark: image recognition, and embedding generation.
- Implemented similarity framework in Spark, which allowed jobs to join datasets based on fuzzy logic.
Technologies: SQL, Scaling, Big Data, PySpark, Kubernetes, Python, NoSQL, BigQuery, Google Cloud Platform (GCP), Snowflake, Data Warehousing, Redshift, MySQL, Data Cleaning
Staff Data Engineer
2019 - 2022
Wizeline
- Drove an internal program to transition software engineers to data. Eighteen graduates were successfully assigned to long-term projects, increasing profit by making engineers billable and filling in project gaps.
- Counseled solution architects in new customer interactions to offer performant data architectures, acquiring new deals and better resource utilization since data architectures and expectations were more realistic.
- 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.
- Collaborated on a change data capture (CDC) pipeline with Delta Lake, Kafka, and Spark, which ingested financial data from other teams and third-party platforms and aggregated information in a data lake ultimately consumed by data scientists.
- Led a data community with over 20 members for over a year. The community was an educational platform where members presented relevant topics to improve the tool's efficiency, deal with real-world errors, and explore data architecture trends.
- Coordinated several mentorship programs aimed at training software engineers in data engineering, thus reducing the demand for the discipline in the Americas.
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
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
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
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
Experience
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, Terraform, Google Compute Engine (GCE), Amazon Athena, AWS Glue, Helm, Qubole, Docker Compose, Jenkins, Grafana, AWS Batch, Apache Iceberg, Apache Druid
Languages
Python, Bash, SQL, Python 3, C++, Java, C, Scala, Snowflake, R, PHP
Frameworks
Spark, Apache Spark, Hadoop, Spring, Flask
Paradigms
ETL, Microservices
Platforms
Linux, Docker, Kubernetes, Google Cloud Platform (GCP), Amazon Web Services (AWS), Apache Kafka, Databricks, Amazon EC2, Buildkite, Azure, AWS Lambda
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, Complex Problem Solving, Teamwork, Data Warehouse Design, Google Cloud Build, ELT, Amazon RDS, Data Analysis, Data Cleaning, Machine Learning, CI/CD Pipelines, Streaming Data, Prometheus, StatsD, Google Cloud Functions, Amazon Kinesis, CDC, Amazon API Gateway, Data Quality, Azure Data Factory (ADF), Amazon EMR Studio
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