- Led and mentored a team of full-stack developers and DevOps engineers, aligning projects with goals and fostering team growth.- Defined project scope, timelines, and resource allocation for data engineering and DevOps, ensuring timely project completion.- Collaborated with data scientists, software engineers, and product managers to identify data needs and design system architectures.- Established a data architecture and DevOps roadmap, selecting the best tools, frameworks, and infrastructures for scalable company growth.- Implemented containerization (Docker) and orchestration (Kubernetes) for scalable deployments on-premises and in cloud environments like AWS, DigitalOcean, and Hetzner.- Leveraged Infrastructure as Code (Terraform, Ansible, CloudFormation) to streamline deployment processes.- Deployed centralized monitoring, logging, and alerting systems (ELK, LGTM stacks) for reliable system performance and proactive issue resolution, ensuring regular stakeholder updates.- Developed and enforced security protocols, including encryption and IAM policies, to protect data pipelines, cloud infrastructure, and DevOps processes.- Optimized cloud costs by balancing performance needs with budget constraints, ensuring cost-effective operations.- Developed backup strategies, failover mechanisms, and disaster recovery plans to ensure business continuity and data resilience.- Continuously evaluated new technologies and methodologies to improve data processing and infrastructure reliability.- Managed technical debt within data and DevOps systems, ensuring long-term maintainability and system efficiency.- Documented systems, architectures, and processes comprehensively to improve onboarding and future reference.- Supported recruitment efforts by assisting the HR team with talent acquisition, building a skilled and cohesive team.

- Collaborated with cross-functional teams, including data scientists, ML engineers, and DevOps engineers, to define data requirements and system architectures.- Managed end-to-end data workflows, ensuring timely and reliable delivery across various platforms.- Implemented and maintained ETL/ELT data pipelines with robust security protocols, including encryption and IAM policies, to ensure data integrity and compliance.- Leveraged Infrastructure as Code (Terraform & CloudFormation) to automate data pipeline deployments and infrastructure provisioning.- Designed backup strategies, failover mechanisms, and disaster recovery plans to ensure data resilience and business continuity.- Monitored and resolved issues in data pipelines using centralized logging, monitoring, and alerting systems (CloudWatch & ELK).- Performed data lineage analysis to validate data pipelines and ensure traceability.- Documented data architectures, processes, and workflows to facilitate future maintenance and team onboarding.- Supported DevOps processes in managing technical debt and ensuring long-term maintainability of data pipelines.- Developed data visualization dashboards and single-page applications (SPAs) using React TypeScript, Gradio, and R Shiny.

- Collected, cleaned, and preprocessed large datasets from diverse sources, ensuring high data quality and usability for analysis and machine learning model development.- Designed, built, and validated machine learning models using appropriate algorithms and techniques to address business problems and drive data-driven decisions.- Identified and engineered relevant features from raw data to enhance model performance, accuracy, and overall predictive power.- Collaborated closely with data analysts, data engineers, and domain experts to understand business needs and translate them into technical specifications for machine learning solutions.- Implemented and maintained production-grade machine learning models, ensuring scalability, reliability, and integration within existing systems.- Developed endpoints for model serving and created multipurpose APIs to support seamless integration of machine learning models into production environments.- Continuously monitored model performance in production, utilizing key metrics to assess model effectiveness and making adjustments as necessary for sustained performance.- Documented all data science workflows, methodologies, and assumptions in detail to ensure transparency, reproducibility, and smooth onboarding for future team members.- Conducted A/B testing and other experimental techniques to validate model effectiveness in real-world applications, ensuring continuous improvement.- Presented insights and findings to stakeholders in a clear and actionable format, translating complex technical concepts into business strategies.- Ensured compliance with data governance standards, maintaining robust data privacy and security throughout the data lifecycle.

- Assisted in the development and implementation of R and Python scripts for data analysis and visualization.- Collaborated with data scientists and analysts to understand data requirements and project objectives.- Performed data cleaning, preprocessing, and transformation tasks to prepare datasets for analysis.- Conducted exploratory data analysis (EDA) to identify trends, patterns, and anomalies in datasets.- Created and maintained documentation for R and Python scripts and data processing workflows, ensuring clarity and reproducibility.- Supported the development of statistical models and machine learning algorithms using R.- Visualized data insights using R and Python packages (e.g., ggplot2, plotly) to produce informative charts, graphs, and dashboards.- Assisted with ad-hoc data analysis requests and reporting as needed.- Contributed to code reviews and collaborative projects, promoting improved coding standards and efficiency.