Engineered and maintained cloud-based infrastructure on AWS, Azure, and GCP, optimized for bioinformatics and genomics research. Developed and deployed scalable pipelines for processing, analyzing, and visualizing complex genomic data, including DNA sequencing and phenotypic data. Created user-friendly interfaces and tools to ease access to cloud-based bioinformatics applications for researchers with varying computational expertise. Implemented data security and compliance measures in line with HIPAA and GDPR to protect sensitive UK Biobank data. Collaborated with multidisciplinary teams to translate scientific research objectives into computational workflows. Optimized cloud resource use and cost efficiency for running large-scale analyses within budget constraints.

Designed and implemented a data visualization dashboard using Tableau to provide actionable insights for optimizing cloud costs and improving resource allocation. Collaborated with multidisciplinary teams, including bioinformaticians, geneticists, and epidemiologists, to understand research objectives, translate complex scientific questions into computational workflows, and troubleshoot technical challenges. Optimized cloud resource utilization and cost efficiency, enabling the execution of computationally intensive analyses at scale while adhering to budgetary constraints.

Blockchain infrastructure setup for blockchain projects. Data systems design and implementation. Development of features across the complete product range, including server-side and client-facing components, to deliver industry-leading solutions.

Led the development of core features for the drone platform, increasing efficiency and safety. Designed and implemented robust, scalable server-side and client-facing software solutions. Collaborated with stakeholders to gather requirements and optimize data flows. Built custom integration APIs for seamless connection with existing client systems. Spearheaded troubleshooting and optimization during product rollout. Mentored and guided junior developers, contributing to their professional growth.

Designed, developed, and maintained scalable web applications using Node.js to ensure efficient logistics management and tracking for Notico's clients. Leveraged ORMs and SQL/NoSQL databases, such as Sequelize, PostgreSQL, and MongoDB, to build robust data models and optimize data access for improved application performance. Collaborated with designers and product managers to refine user interfaces and incorporate new features, balancing functionality with user experience.

Engaged in the development of an innovative investment data analysis platform for venture capital firms, employing AI and machine learning. Designed and implemented robust data pipelines for ingesting, cleaning, and transforming large volumes of financial, market, and startup data from various sources. Developed and deployed machine learning models for predictive analytics to identify promising investment opportunities based on historical patterns, industry trends, and financial performance. Built intuitive data visualizations and interactive dashboards to present complex investment insights in a clear and actionable manner, empowering venture capitalists to make informed decisions. Collaborated with data scientists and domain experts to refine algorithms, optimize model performance, and ensure the accuracy and relevance of insights generated by the platform.

Engineered and maintained robust data pipelines, implementing Apache Airflow to orchestrate complex data integration workflows, improving data reliability and reducing manual intervention. Designed and deployed scalable cloud infrastructure on AWS using CloudFormation, leveraging Lambda functions, EC2 instances, Fargate containers, Aurora databases, Athena for serverless querying, and Redshift for data warehousing. Implemented CI/CD pipelines with GitHub, Jenkins, Docker Compose, and Docker to automate code testing, building, and deployment, ensuring rapid and reliable software delivery. Integrated monitoring and logging tools (DataDog, Sentry) to proactively identify and resolve performance bottlenecks and system errors, enhancing overall system stability. Developed backend components of data processing applications using the Python Django framework, contributing to efficient data management and analysis. Utilized NEO4J graph database and Cypher/OpenCypher query language to model complex relationships within datasets, enabling deeper insights and analysis.

Led full-stack development of IoT applications, leveraging LoopBack (Node.js) and Python for robust backend services and integrating diverse sensor data for real-time monitoring and analysis. Applied machine learning (MXNet, TensorFlow) and data mining (Orange, RapidMiner) techniques to develop intelligent algorithms for predictive maintenance, anomaly detection, and optimization of IoT systems. Integrated message brokers (RabbitMQ, Kafka) to enable seamless communication and data exchange between IoT devices, backend services, and frontend applications. Built scalable and secure IoT infrastructure using Kong API gateways, Docker containers, and AWS EC2 instances, ensuring efficient data processing and system reliability. Implemented CI/CD pipelines with GitLab and Node.js PM2, enabling automated testing and deployment for faster development cycles and improved code quality. Managed and optimized MariaDB and Galera clusters for high-performance data storage and retrieval, ensuring data integrity and availability for critical IoT applications.