Vladislav Lasmann

Designed and implemented a scalable data lake on Google Cloud Platform (GCP) for the Anti-Financial Crime (AFC) division, supporting Anti-Money-Laundering and Counter-Terrorism-Financing initiatives

Built robust data pipelines for ingesting and processing over 30 million daily transactions from retail and corporate banking, applying dimensional modeling to enable advanced analytics and regulatory reporting

Ensured resilience by implementing change data capture, late-data handling, and schema evolution across evolving source systems

Led a small engineering team and collaborated with international stakeholders in Germany, Singapore, Sri Lanka, Hong Kong, Taiwan, and Thailand to ensure compliance, data quality, and performance

Google Cloud Platform (GCP)

Apache Spark (Dataproc & Cloudera Data Platform - Python PySpark)

Apache Airflow (Cloud Composer - Python API)

Google Cloud Storage

BigQuery

GitHub Actions

Terraform

Google Kubernetes Engine (GKE)

Developed an automated, highly available near-real-time system for monitoring and evaluating deliveries across Europe

Azure DevOps

Azure DataFactory

Azure Data Lake

Apache Spark (Python PySpark via Databricks)

Apache Kafka

Apache Hive

Delta Lake

ADLS Gen2

Unity Catalog / Databricks SQL

Microsoft SQL Server

Apache Kafka

Azure DevOps Pipelines

Microsoft PowerBI

Terraform

Developed components for automated smart contract analysis and document generation on a platform for smart contract analytics

Integrated raw machine learning models into production workflows

Spring-Boot

Python Flask

Python libraries for PDF feature extraction and generation

MinIO & S3 Storage

Postgres Database

Keycloak User Authentication

Kubernetes

Docker-Compose

GitActions for Continuous Integration & Deployment

Completed two freelance projects focused on financial market analysis and investment strategy development

Collected and integrated data from third-party providers and crawled financial data from various websites

Built distributed data storage infrastructure using PostgreSQL, MongoDB, and Neo4j, connected to an Apache Spark processing cluster

Developed an end-to-end data pipeline and implemented a software environment for backtesting and portfolio rebalancing

Created a scalable and user-friendly web application for strategy selection and simulation

Acted as Scrum Master, managing agile workflows and facilitating team coordination

Third Party REST APIs

WebCrawling using Python–Scrapy and Python-BeautifulSoup

Postgres

Neo4J

Sharded MongoDB

Cassandra

ApacheSpark

Python–Pandas

Python–Django

Python–Flask

Kubernetes

Docker-Compose

Jenkins