• Design and maintain the analytical platform (OLAP) by leveraging the data-engineering (foundational work) & business-intelligence teams (reporting systems).
• Align with stakeholders and coordinate with data science managers to provide timely, resilient, and scalable solutions for the business.
• Hands-on data engineering manager that leads by example, promoting best coding practices and working side-by-side with data engineers when needed.

Skills: Data Engineering · Big Data · Python · SQL · Amazon Web Services (AWS) · Leadership · Apache Spark

• Hands-on technical leadership role.
• Data platform design in AWS, Databricks, and Snowflake.
• Coordinate and mentor the data-engineering team on implementing technical solutions.
• Lead the technical implementation of the data platform (OLAP environment).
• Managed a centralized data-team with company-wide stakeholders.
• Ensure best coding practices, high-quality deliveries, and technical proficiency across the team.

Skills: Data Engineering · Machine Learning · Python · SQL · Amazon Web Services (AWS) · Apache Spark

• Development & support of the OLAP environment: Data Lake (e.g., S3, parquet, delta tables) & Warehouse (Snowflake, dimensional data modeling).
• Data replication (CDC) & integration ETL/ELT development with pyspark.
• Implemented a serverless architecture in AWS Cloud: Lambda, SQS, etc.
• Developed a set of internal tools and python libraries (e.g., machine-learning support libraries for data scientists).
• Data modeling and business automation.
• Contribute on R&D of new financial products.

• Created the data processing pipeline from scratch using Scala & Apache Spark.
• Created a search interface based on Django and Elasticsearch.
• Business data-modeling and data-quality assessments.

Proactive member of the data-engineering team and Scala developer with a focus on functional programming, big data, and ML.

• Contributed to the internal definition of the big-data carrer path.
• Teaching scala and big-data tools (i.e. Spark) to internal consultants.
• POCs using a big-data tech stack (Akka Streaming, Kafka, Cassandra, Spark).

The pricing model proposal at Crabi is known as UBI (usage-based insurance). This means that the insurance premium is calculated according to the user’s behavioral data (generated by telematics devices, mobile app, social network, etc.). The initial work of this model begins with generalized linear models and scales up to machine learning algorithms. Among my main responsibilities:

• Contribute to the development of a risk-model based on behavioral data.
• Real-time data processing platform using Spark, Kafka, and Cassandra.

I began my internship at Crabi while it was an early-stage startup in R&D. My main responsibilities were:

• Data analytics of IoT devices used to track test-users.
• Automated business requirements with Python and R.
• POC chatbot for FB-messenger.