object storage , Kubernetes), Containers and orchestration (Docker, Python, Distributed query and compute engines, Model registries, Efficient columnar and array data formats, Large-scale data systems, CI/CD for ML

data wrangling , Ml, Data Analytics, Optimization Algorithms, Python, cleaning and validation, Ai, ML libraries, modern AI

Pyspark, Apache Spark, Kafka, Sql, Git, Databricks, Python, RAG solution stacks, embeddings, CI CD workflows, vector databases, cloud-native observability, Delta Lake

data engineering , Api Integration, Sql, Python, Data Warehousing, R, AI algorithms, On-premise systems, Data pipelines, data models

Containerization (Docker), Version control (Git), Java, Python, Data lakes, Data Catalog, Vector databases, NoSQL databases, LLM data infrastructure, CI/CD practices

Lambda, Aws Services, Python, Sql, Etl, Distributed System Design, cloud ETL technology, EKS

Web Services, Powerbi, Xml, Etl Tools, Json, Restful Apis, middleware, Azure Data Lake Storage, integration platforms, Azure cloud platform, Azure SQL Database

Sql, Python, AI LLM-based systems, analytics infrastructure, data pipelines, Agent Development Kit, Cider Agents, data architecture design, enterprise-scale data platforms, Gemini Command-line Interface, LLM Extensions, machine learning operations

Data Modelling, Pyspark, Databricks, Python, Etl, ELT, data pipeline development, Databricks Workspace, Delta Lake, Lakehouse architecture, AI Agent