Hi, I'm Asaniczka

What I Bring to the Table

I'm a seasoned Data Engineer and Kaggle Grandmaster with a passion for building scalable, production‑grade data systems that fuel innovation and drive business insights. My journey in data engineering has been marked by hands‑on experience designing robust ETL workflows, architecting distributed data pipelines, and leveraging cloud infrastructures to manage data at scale.

• Scalable Data Pipelines & Distributed Systems: I specialize in designing and implementing both batch and real‑time data pipelines using industry‑standard tools such as Apache Spark, Kafka, Flink, and Delta Lake. I build high‑performance systems that seamlessly handle large volumes of data.
• Robust Data Modeling & Quality Assurance: I excel at creating efficient data models for relational and columnar databases (e.g., PostgreSQL, Redshift, BigQuery) with a strong focus on data quality, governance, and compliance—empowering teams to make informed, data‑driven decisions.
• Cloud & Production‑Grade Infrastructure: With deep expertise in AWS (S3, Redshift, EMR, Glue, etc.), Microsoft Azure (Synapse, Azure Fabric), and Google Cloud (BigQuery), I design secure, scalable, and cost‑effective data solutions that perform reliably under heavy loads.
• Cross‑Functional Collaboration & Leadership: I thrive in dynamic, collaborative environments, working closely with product managers, data scientists, and ML engineers to drive innovation. I lead by establishing best practices and mentoring teams to continuously improve data engineering processes.

Technical Skills & Tools

• Cloud & Storage: AWS S3, Redshift, DynamoDB, EMR; Azure Synapse, Azure Fabric; Google BigQuery; Snowflake; Databricks
• Data Processing: Apache Spark, Kafka, Flink, Delta Lake, Apache NiFi, Apache Hudi, Apache Iceberg
• Programming Languages: Python, Go, Scala, Java, JavaScript, TypeScript
• Databases: PostgreSQL, MySQL, Cassandra, MongoDB, Redis, Neo4j, ClickHouse
• ML & AI: ML algorithms (linear & logistic regression, decision trees, random forests, SVM, KNN, AdaBoost, XGBoost, CatBoost); Deep Learning (CNNs, RNNs, GANs, transfer & reinforcement learning) using PyTorch, TensorFlow 2.0, Hugging Face Transformers
• BI & Visualization: Tableau, Looker Studio, Power BI, Grafana, Plotly, Dash
• DevOps & Web Servers: Docker, Kubernetes, Terraform, NGINX, Git, CI/CD