Process and analyze high-volume blockchain data from Solana and EVM ecosystems, ensuring accuracy and reliability.Scaled and optimized ETL processes by implementing parallelized workflows, improving data throughput and processing efficiency.Architected and deployed AWS infrastructure (ECS, Lambda, Load Balancers, Registries) using Terraform to enhance system scalability and resilience.Collaborated with frontend and full-stack developers to improve database and API performance, reducing latency and boosting end-user experience.Designed, optimized, and maintained complex SQL queries in PostgreSQL to handle millions of records efficiently.Evaluated and integrated data providers and RPC services, enhancing the platform's reliability and data accessibility.

Built and deployed a data infrastructure on Google Cloud Platform (GCP) from scratch, incorporating services like GKE, Cloud SQL, GCE, and Load Balancers, leveraging Terraform and Ansible for automation.Developed and deployed Python APIs using FastAPI on Cloud Run, enabling efficient data integration and scaling capabilities.Streamlined data ingestion pipelines with Airbyte, ensuring seamless extraction from diverse sources into centralized data warehouse (BigQuery).Designed and implemented data transformations using DBT, enhancing data quality and enabling actionable analytics.Automated workflow orchestration and scheduling with Apache Airflow, improving pipeline reliability and reducing manual overhead.Established CI/CD pipelines using GitHub Actions and Docker, optimizing deployment processes and ensuring robust code delivery.

Built and managed data pipelines to ingest data from internal databases and third-party tools into AWS Redshift, leveraging Stitch (managed EL), AWS Glue, and Python with Singer frameworks.Automated data workflows and job scheduling using Apache Airflow, ensuring timely and reliable data delivery.Designed and implemented data transformation workflows using DBT to standardize and structure data for analytics.Created insightful dashboards and visualizations with Looker, enabling data-driven decision-making across teams.Deployed infrastructure as code using Terraform to maintain consistency and scalability in the cloud environment.

Lead for Business Intelligence and Data Engineering team to design, build, and optimize real-time data pipelines and analytics platforms.Implemented solutions using Apache Flink and PySpark to process high-volume data streams, ensuring streaming delivery for fraud detection and usage monitoring.Database modeling and query optimization across diverse storages, including HDFS and DynamoDB.Initiated projects leveraging Google Cloud Platform (GCP) and AWS to deliver scalable, cloud-native solutions.

Industrialized machine learning use cases by deploying models and data workflows with Apache Spark and Flink.Developed robust data pipelines using Python and Java, integrating message brokers like Kafka and RabbitMQ for seamless data sharing.Utilized Hive and Impala for querying large datasets and optimizing distributed data storage systems.Deployed and maintained cloud-based solutions on AWS (S3, EC2, ALB), enhancing scalability and performance of data processing platforms.

Developed a framework to industrialize data science use cases, including preprocessing, modeling, and deploying ML workflows.Built machine learning models using Spark ML and Scikit-Learn, delivering actionable insights from large-scale data.Deployed Apache Spark applications on a Hadoop cluster to optimize big data workflows.

Research on the Similarity Flooding algorithm (Sergey Melnik), exploring its application in graph matching and similarity measures for fraud detection in the insurance industry.Developed and tested graph-based algorithms using Java, Scala, and the Apache Spark framework to process and analyze large-scale datasets.Collaborated with Allstate Northern Ireland to apply research findings to practical use cases, enhancing fraud detection methodologies.