Axel Furlan
Verified Expert in Engineering
Data Engineering Developer
Buenos Aires, Argentina
Toptal member since October 21, 2024
Axel is a senior data engineer with 6+ years of experience building and optimizing data solutions. Proficient in Python, SQL, cloud platforms, Apache Airflow, data build tool (dbt), Kubernetes, and Terraform, he has a proven track record of designing and implementing scalable data infrastructures for various industries. As a data infrastructure expert, Axel has successfully worked on the Toptal core team.
Portfolio
Experience
- Python - 10 years
- SQL - 10 years
- Data Engineering - 6 years
- Apache Airflow - 6 years
- Amazon Web Services (AWS) - 4 years
- Google Cloud Platform (GCP) - 4 years
- BigQuery - 3 years
- Snowflake - 2 years
Availability
Preferred Environment
MacOS, Python, Google Cloud Platform (GCP), Amazon Web Services (AWS), Snowflake, SQL, Google BigQuery, Docker, Kubernetes
The most amazing...
...data infrastructure I've implemented for a New York eCommerce client enabled the board to see relevant metrics on-demand.
Work Experience
Senior Data Engineer
Toptal
- Deployed our Cloud Composer environments (development, staging, and production) using Terraform.
- Deployed CI/CD pipelines to test and check any errors on DAGs and deployed them when a new release was made.
- Migrated multiple Python pipelines from Luigi to Airflow.
- Led and implemented initiatives for cost reduction on the cloud. Reduced storage costs from 20,000 to 6,000 annually (70% reduction).
- Performed research regarding new possible data engineering tools to add to our stack and presented those results to the team.
- Applied encryption and tag policies for sensitive data (PII) in BigQuery.
Senior Data Engineer
Distillery
- Coded over 50 ETLs, implementing Python, Airflow, and PySpark (with EMR), and making use of Redshift and Snowflake.
- Migrated our Redshift datasets to Snowflake, leveraging Snowflake's top-tier features.
- Implemented Terraform on our new Snowflake infrastructure, managing users, roles, databases, and integrations.
- Trained the data engineering team to use Terraform to apply changes through IaC.
- Designed and implemented an architecture to solve load-related errors that our pipelines were having, posting data to our CRM. I implemented a queue (AWS SQS) and async ETL to post updates on the CRM.
Data Architect
Thirstie
- Designed, pitched, and led the implementation of the company's data solutions for both internal and external use, leveraging the AWS cloud with ECS, Docker, Python, and Airflow.
- Led a 2-person team: a junior DE and a data analyst.
- Implemented the data warehouse. We had a MySQL DB manually deployed in a bare metal server. Moved to a managed solution in AWS Redshift.
- Migrated our data services to containerized solutions. Again, services were manually deployed, accessing the server using SSH. I implemented containers and used AWS ECS to deploy the Airflow setup and Metabase.
- Implemented a client-facing solution for the eCommerce clients to see their own data using Metabase.
- Reduced huge report query times from 15 minutes to just 3 seconds by analyzing the query plan (EXPLAIN ANALYZE). This was attained in different cases by making use of indexes, materializations, query planning, and denormalizations.
Experience
Data Infrastructure for Thirstie
This whole infrastructure was very cost-savvy since I didn't need to use any managed services apart from ECS to deploy containers. The infrastructure was resilient, and I'm confident that we only had outages when the AWS cloud collapsed.
Education
Master's Degree in Software Engineering
Universidad Tecnológica Nacional - Buenos Aires, Argentina
Skills
Libraries/APIs
Pandas, PySpark, Luigi
Tools
Apache Airflow, Git, BigQuery, Google Cloud Composer, Terraform, Amazon Elastic Container Service (ECS), AWS CloudFormation, RabbitMQ
Languages
Python, SQL, Snowflake
Paradigms
ETL
Platforms
Google Cloud Platform (GCP), Amazon Web Services (AWS), Linux, MacOS, Docker, Kubernetes, Amazon EC2, Databricks
Storage
PostgreSQL, Data Pipelines, Google Cloud, Redshift, Amazon S3 (AWS S3)
Frameworks
Spark, Flask
Other
Data Engineering, Data, Software Engineering, Data Architecture, CI/CD Pipelines, Google BigQuery, Engineering Software, Computer Science, EMR, ECS, Infrastructure as Code (IaC)
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