Axel Furlan, Developer in Buenos Aires, Argentina
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Axel Furlan

Bio

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

Onelead
Go, Python, Node.js, Heroku, Docker, LangChain, OpenAI
Amperon
Python, Argo Workflows, Kubernetes, ClickHouse, Azure, Data Warehousing
Toptal
Python, Apache Airflow, Google Cloud Composer, BigQuery...

Experience

  • SQL - 10 years
  • Python - 10 years
  • Apache Airflow - 6 years
  • Data Engineering - 6 years
  • BigQuery - 4 years
  • Google Cloud Platform (GCP) - 4 years
  • Amazon Web Services (AWS) - 4 years
  • Snowflake - 2 years

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

Co-founder Software Engineer

2025 - PRESENT
Onelead
  • Developed the main agent using Node.js, integrated it to WhatsApp using Twilio, and coded the tools it used.
  • Developed the vectorized database and used RAG for the agent to determine the best real estate property the lead is looking for.
  • Applied cache techniques in Node.js to keep agent server responses under four seconds.
Technologies: Go, Python, Node.js, Heroku, Docker, LangChain, OpenAI

Senior Data Engineer

2025 - PRESENT
Amperon
  • Deployed Apache Superset in Kubernetes and created the first dashboards for internal use.
  • Reduced the number of failures of the main data pipeline that produced the company's clients' data and reports.
  • Reduced the cost of workflows by utilizing Azure metrics and Prometheus to determine how much actual resources each job used.
Technologies: Python, Argo Workflows, Kubernetes, ClickHouse, Azure, Data Warehousing

Senior Data Engineer

2022 - 2024
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.
Technologies: Python, Apache Airflow, Google Cloud Composer, BigQuery, Google Cloud Platform (GCP), Luigi, SQL, PostgreSQL, Terraform, Data Engineering, Pandas, Linux, ETL, Data, Data Architecture, CI/CD Pipelines, Software Engineering, Data Pipelines, Google Cloud, Google Cloud Storage, Google Kubernetes Engine (GKE), Parallel Computing, Data Warehousing, Google BigQuery

Senior Data Engineer

2021 - 2022
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.
Technologies: Python, Apache Airflow, Amazon Web Services (AWS), Snowflake, Redshift, Spark, EMR, Infrastructure as Code (IaC), PostgreSQL, Data Engineering, Pandas, PySpark, RabbitMQ, Linux, ETL, Data, Data Architecture, Databricks, CI/CD Pipelines, Software Engineering, Data Pipelines, Google Cloud Storage, Google Kubernetes Engine (GKE), Parallel Computing, Data Warehousing

Data Architect

2019 - 2021
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.
Technologies: Python, Amazon Web Services (AWS), ECS, Docker, Apache Airflow, AWS CloudFormation, PostgreSQL, Data Engineering, Pandas, Linux, ETL, Data, Data Architecture, CI/CD Pipelines, Software Engineering, Data Pipelines, Parallel Computing, Data Warehousing

Experience

Data Infrastructure for Thirstie

I was the data engineer behind the whole company's infrastructure. I deployed Apache Airflow in Amazon Elastic Container Service (ECS) when managed Airflow wasn't a thing. I developed all our data pipelines there and deployed dbt so the data analysts could add their data marts through a Git repo. This project also involved applying CI/CD to the repo to deploy those new models to the Redshift cluster when merged with the main branch. I also deployed an open-source data visualization tool (Metabase) for data analysts to create dashboards.

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

2014 - 2022

Master's Degree in Software Engineering

Universidad Tecnológica Nacional - Buenos Aires, Argentina

Skills

Libraries/APIs

Pandas, PySpark, Luigi, Node.js

Tools

Apache Airflow, BigQuery, Git, Google Kubernetes Engine (GKE), Google Cloud Composer, Terraform, Amazon Elastic Container Service (ECS), AWS CloudFormation, RabbitMQ

Languages

Python, SQL, Snowflake, Go

Paradigms

ETL, Parallel Computing

Platforms

Google Cloud Platform (GCP), Amazon Web Services (AWS), Linux, MacOS, Docker, Kubernetes, Amazon EC2, Databricks, Azure, Heroku

Storage

PostgreSQL, Google Cloud Storage, Data Pipelines, Google Cloud, Redshift, Amazon S3 (AWS S3), ClickHouse

Frameworks

Spark, Flask

Other

Google BigQuery, Data Engineering, Data, Software Engineering, Data Warehousing, Data Architecture, CI/CD Pipelines, Engineering Software, Computer Science, EMR, ECS, Infrastructure as Code (IaC), Argo Workflows, LangChain, OpenAI

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