*Designed and implemented ETL pipelines using Azure Data Factory (ADF) and SQL Server Integration Services (SSIS), optimizing data integration and transformation processes.*Demonstrated proficiency in cloud-based data solutions, leading and executing data management projects on Azure Cloud.*Translated complex analytical requirements into technical designs, including data models, ETLs, and dashboards, enabling data-driven decision-making.*Developed scalable end-to-end architecture leveraging Azure Components like HDInsight, Data Factory, Data Lake, and Storage to address business challenges.*Collaborated with data scientists to develop and deploy machine learning models into production using TensorFlow, PyTorch, and sci-kit-learn.*Managed data processing infrastructure using containerization technologies like Docker and Kubernetes, orchestrating data workflows with Apache Beam and Apache Flink.*Built and maintained real-time and batch data pipelines using Apache Kafka, Apache Airflow, and AWS Lambda.*Conducted data profiling and analysis to identify trends and insights, automating data analysis through custom Python scripts and SQL queries.*Designed and executed ETL workflows using Python and SQL to process and store large volumes of structured and unstructured data.*Developed data models and architectures for various projects, optimizing query performance using SQL and NoSQL databases, and ensuring data accuracy through automated data quality checks.*Integrated data from MongoDB, MS SQL, and cloud (Blob, Azure SQL DB) using Azure Data Factory and SQL API.*Created JSON Scripts for deploying Pipelines in Azure Data Factory (ADF) to process data using SQL Activity.*Developed and maintained end-to-end data architecture for various projects, ensuring data accessibility and user autonomy.* Designed and implemented data warehousing systems using Snowflake and Redshift, optimizing query performance through indexing and partitioning techniques.

•Developed intricate SQL and NoSQL queries for data extraction, transformation, and loading (ETL) processes, optimizing for performance on large datasets.•Utilized advanced data modelling techniques to transform raw data into clean, tested, and reusable datasets, enabling efficient data analysis and business intelligence.•Applied complex data transformations using SQL and Python, including data cleaning, normalization, aggregation, and summarization to prepare data for analysis.•Maintained comprehensive data dictionaries and metadata repositories to ensure consistent definitions, data lineage, and language across the team.•Defined and implemented data quality rules, standards, and metrics using data validation and cleaning scripts, ensuring data accuracy and consistency across all data assets.•Applied software engineering best practices to analytics, including version control with Git, data unit testing, and continuous integration and continuous delivery (CI/CD) using tools like Jenkins and Docker.•Leveraged BI tools like Tableau and PowerBI for data visualization, creating interactive dashboards, graphs, charts, and reports to communicate data insights effectively.•Collaborated closely with data engineers, business analysts, and data scientists to align business requirements with data assets, often translating between technical and non-technical stakeholders.•Managed data migration projects, moving enterprise data into cloud-based data warehouses like Amazon Redshift or Google Big Query.•Utilized programming languages like Python and R for advanced data orchestration tasks, including data scraping, automation, and machine learning model development.•Worked with dbt (data build tool) for implementing analytics code using SQL, creating reusable and maintainable data transformation pipelines.•Demonstrated proficiency with data engineering and BI tools like Snowflake, AWS Glue, Looker, and others for data pipeline construction and business intelligence.