As a Data Platform Engineer, I specialize in building scalable, reliable, and production-grade data systems that power data-driven insights. With a strong foundation in core software engineering and distributed systems, I bring a systems-thinking approach to solving complex data challenges. I design and develop end-to-end ETL/ELT pipelines, manage workflow orchestration using Apache Airflow and Prefect, and work across both OLAP and OLTP systems. My focus areas include modern data architectures, data observability, and automation—leveraging technologies and tools such as Python, Java, Spark, Talend, Kafka, DBT, Soda, Alembic, Postgres, Snowflake, Clickhouse and Kubernetes. I'm experienced in: Designing and implementing modular data pipelines, data lakes, and warehouse solutions. Building with the modern data stack to support batch and near real-time processing. Applying dataOps best practices, including CI/CD for data workflows, monitoring, managing deployments and testing. Working with dimensional and vault data modeling techniques for scalable analytics. Ensuring data quality, lineage, and SLA adherence through observability tools and frameworks. Passionate about continuous improvement, I enjoy working with evolving cloud-native and hybrid data stacks (Snowflake, BigQuery, Delta Lake, Iceberg, etc.) and applying agile practices to build resilient data infrastructure. Let’s connect to talk about building modern, scalable, and intelligent data systems.