Led a high-performing data engineering team, overseeing end-to-end data pipeline development, optimization, and maintenance.Orchestrated data ingestion from diverse sources into a data lake, implementing transformations such as sorting, joining, aggregations, and filtering on AWS infrastructure.Architected Airflow Directed Acyclic Graphs (DAGs) to automate data flow, facilitating seamless migration from legacy Teradata to MongoDB and enhancing data accessibility.Spearheaded the development of robust ETL pipelines leveraging Spark, extracting data from SQL Server, Hive, and other sources, and orchestrating data processing triggers on Amazon S3 using AWS Lambda and Glue.Proactively provisioned and managed Databricks clusters to enable efficient batch and real-time data processing, optimizing resource utilization and runtime efficiency.Designed and implemented a comprehensive data quality framework utilizing Apache Beam, ensuring data quality metrics collection for Teradata and Mongo DB databases.Successfully integrated Apache Kafka for real-time streaming data processing, leveraging custom listener processes and enhancing the organization's real-time data capabilities.Collaborated with cross-functional teams to define data requirements and provide technical guidance on the design and deployment of Azure Synapse solutions, ensuring secure and scalable enterprise-level data management.Orchestrated the integration of Power BI with various data sources, including AWS S3, Snowflake, Azure Synapse, MongoDB, Teradata, SQL Server, and Hive, enabling comprehensive data visualization.Developed Power BI integration with Apache Kafka for real-time insights generation, empowering decision-makers with up-to-the-minute data-driven insights.Provided training and mentorship to team members, fostering their technical growth and contributing to the overall improvement of the data engineering capabilities within the organization.

Demonstrated exceptional leadership as the head of the data engineering team, guiding a team of skilled engineers to deliver high-quality data solutions.Formulated and executed a data migration strategy, successfully transferring data from various legacy systems to modern cloud-based platforms like AWS, Snowflake, and Azure Synapse.Collaborated with stakeholders to define data architecture standards, resulting in consistent and efficient data storage, processing, and retrieval across the organization.Designed and implemented a scalable data lake architecture, enabling the ingestion, storage, and processing of diverse data types, leading to enhanced data analysis capabilities.Oversaw the implementation of complex data transformations, such as data enrichment, data normalization, and data aggregation, improving data accuracy and relevance for business analytics.Drove the adoption of best practices for data quality management, ensuring that data was accurate, complete, and timely for all downstream processes.Led the deployment of data pipelines using Apache Airflow, orchestrating intricate workflows and scheduling data processing jobs, resulting in streamlined and automated data movement.Implemented data governance measures, establishing data access controls, data lineage tracking, and data ownership accountability, in compliance with industry regulations and organizational policies.Successfully integrated Power BI with real-time streaming data sources, enabling the creation of dynamic and interactive dashboards that provided actionable insights in real time.Championed the use of advanced analytics techniques, such as machine learning and predictive modeling, to unlock deeper insights from data and drive data-driven decision-making.Collaborated with the IT security team to implement data encryption, data masking, and other security measures, safeguarding sensitive data and ensuring data privacy.

Proficient in SQL, Python, and Apache Spark for data integration, transformation, and cleansing.Successfully migrated on-premises data infrastructure to cloud-based platforms like Snowflake, AWS Redshift, and Google BigQuery.Deep expertise in Apache Spark, including real-time streaming and batch processing, for large-scale data workloads.Designed end-to-end ETL pipelines using Apache Airflow, automating complex workflows and ensuring data consistency.Developed custom Python scripts for data orchestration, transformation, and quality assurance checks.Created optimized data models, schemas, and tables using SQL, enhancing data storage and retrieval efficiency.Engineered real-time data streaming solutions with Apache Kafka and Spark Streaming, enabling low-latency processing.Collaborated with data science teams to integrate machine learning models into data processing workflows.Established data quality checks and governance processes, ensuring accurate and consistent data.Provided technical leadership and mentorship to junior data engineering team members.Leveraged cloud services (AWS/Azure/GCP) for data storage, processing, and integration, optimizing cost and performance.Implemented CI/CD pipelines for automated deployment of data pipelines and infrastructure-as-code.Identified and resolved performance bottlenecks, improving data processing speed and efficiency.Effectively communicated complex technical concepts to non-technical stakeholders, fostering clear understanding.Documented data pipelines, ETL processes, and architectural decisions for knowledge sharing and reference.

1.I have hands-on experience with Microsoft's suite of cloud-based data storage and analytics services. I am comfortable working with these tools to store, process, and analyze large datasets, and I understand how to use them effectively to support business intelligence and data science applications.2. Programming Languages: Proficient in programming languages such as Java, Python, and C#.3. I am versatile and able to work with different programming languages as needed. I have experience with object-oriented programming and understand the strengths and weaknesses of each language, allowing me to choose the best one for a given task.4. Database Management: Familiar with database management systems such as MySQL, Oracle, and PostgreSQL.5. I have a basic understanding of database concepts and can work with popular database management systems. I know how to design, create, and maintain databases, as well as perform basic querying and reporting tasks.6. Operating Systems: Proficient in Windows and Linux operating systems.I am comfortable working in both a Windows and Linux environment, and I understand the key differences between these platforms. I can install, configure, and troubleshoot issues on either system, ensuring seamless compatibility and functionality.

SDLC: Good understanding of the entire software development life cycle, including testing methodologies, disciplines, tasks, resources, and scheduling.Azure Data Services: Proficient in working with Azure Data Factory (ADF), Azure Data Lake (ADL), Azure Data Lake Analytics, Azure SQL database, Azure Databricks, Azure Synapse, Azure SQL Data Warehouse, and Azure BLOB Storage.Programming Languages: Proficient in programming languages such as Java, Python, and C#.Database Management: Familiar with database management systems such as MySQL, Oracle, and PostgreSQL.Operating Systems: Proficient in Windows and Linux operating systems.