With more than two years of experience in data engineering, I build modern big-data pipelines and workflows with a wide range of technologies.

My background includes:

• Building ETL/ELT pipelines across diverse platforms and frameworks
• Deploying and operating data platforms on AWS (S3, Glue, Athena, Lambda, etc)
• Orchestrating distributed workloads with PySpark and Airflow
• Developing high-quality Kafka producers and consumers
• Designing efficient warehouses and query systems on Snowflake and other modern OLAP tools
• Creating stakeholder-focused visualizations in AWS QuickSight, Grafana, Dash, and Streamlit
• Setting up CI/CD pipelines to automate and safeguard data workflows

What sets me apart is not just my technical toolkit but my commitment to continuous learning and adaptability. Whether I’m defining data contracts, extending CI/CD practices, or adopting new architectural patterns, I pursue the most effective solution for each challenge. I’m especially driven to deliver end-to-end data solutions that maximize business value.

Even though most of my professional work has been in data engineering, my Master’s in Data Science gave me a solid foundation in machine learning. Through courses and side projects, I’ve had the chance to design and build solutions across the Machine Learning space:

Technical strengths:

• Supervised learning: end-to-end pipelines using scikit-learn, XGBoost, LightGBM, Random Forests — including feature engineering, tuning, and evaluation
• Unsupervised learning: word embeddings (Word2Vec, GloVe), topic modeling (LDA, BERTopic), and recommender systems (both collaborative and content-based)
• Deep learning: building and fine-tuning CNNs, RNNs/LSTMs and Transformer models with PyTorch
• MLOps fundamentals: MLflow for experiment tracking, DVC for versioning and deploying containerized prototypes on AWS (ECR/ECS)
• Project mindset: starting with quick prototypes in notebooks, then turning them into production-ready code and making sure insights are clearly documented for non-technical teams

I haven’t had an official “Machine Learning Engineer” title yet, but I keep taking on ML-related projects. From sports analytics models to NLP experiments, I like approaching new challenges with curiosity and a focus on making research ideas work in real-world settings.

My focus on data architecture complements my engineering and ML skills, enabling me to translate business requirements into resilient, high‑performance data platforms, always trying to select the best design patterns and architectural approach for the problem at hand.

Core capabilities:

• System design & architecture: blueprinting end‑to‑end solutions that balance scalability, cost, and maintainability
• Data modeling & lake‑house patterns: crafting relational and dimensional models, plus lake architectures (e.g., S3 + Iceberg) that support both analytics and ML workloads
• ML‑ready pipelines: integrating feature stores, model‑training jobs, and real‑time/ batch inference into unified workflows
• Cloud infrastructure on AWS: provisioning secure, elastic stacks with services like S3, Glue, Athena, EMR, Lambda, and Redshift
• Docker containers: containerizing applications for consistent and repeatable deployments.

Continuous improvement mindset:

• Expanding DevOps proficiency with Terraform/IaC, Jenkins CI/CD, and Kubernetes for container orchestration
• Evaluating emerging frameworks: Apache Iceberg, Delta Lake, dbt and data‑contract tooling—to adopt best‑in‑class patterns
• Applying proven design principles (e.g., medallion, domain‑driven design, event sourcing) to ensure each architecture fits the problem rather than forcing a one‑size‑fits‑all approach

I actively pursue new technologies and architectural patterns, always aiming to deliver data platforms that are reliable today and adaptable for tomorrow’s demands.

My software‑engineering background rounds out my data skill set, letting me deliver end‑to‑end products that are as dependable as they are user‑friendly.

Key strengths

• Full‑stack development: modern web/mobile apps and APIs (REST, GraphQL, Webhooks) with React/React native, Astro, and Next.js on the front-end, paired with Django, Node.js and FastAPI on the back-end
• Engineering fundamentals: solid grounding in data structures & algorithms, software‑design patterns and agile methodologies
• Quality & reliability: comprehensive testing strategies, CI/CD pipelines, and automated deployments for rapid yet safe iteration
• Operations: microservices architecture and Docker‑based containerization for scalable, maintainable services
• Lifecycle stewardship: clear documentation, observability, and structured maintenance processes that keep applications healthy in production

I continually evaluate new languages, frameworks, and architectural patterns to ensure each solution is the best possible fit for the problem at hand.