Sushmitha Sekar

Experienced Azure Data Engineer with 4 years of hands-on experience in designing, implementing, and optimizing data solutions on the Azure platform
Proven track record of managing end-to-end data engineering solutions on the Azure cloud platform, leveraging services such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics
Extensive experience in architecting and optimizing data pipelines for scalable data ingestion, transformation, and processing, resulting in improved data quality, performance, and reliability
Proficient in programming languages such as Python, SQL, and Scala, with hands-on experience in building complex ETL/ELT workflows, data cleansing routines, and analytical models to extract insights from large volumes of structured and unstructured data
Skilled in data modeling, schema design, and data warehousing concepts, with expertise in designing and implementing data storage solutions using Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Data Warehouse
Strong understanding of distributed computing principles and big data technologies, including Apache Spark, Hadoop, and Kafka, with a focus on optimizing data processing tasks for parallelism and performance
Proficient in using Azure technologies for ETL processes, leading to enhanced performance, data quality, cost savings, and improved data governance
Innovatively skilled Azure Data Engineer adept at ensuring high data accuracy and consistency throughout end-to-end processes, specializing in Snowflake data warehouse integration and implementing data privacy compliance using Azure Cosmos DB
Knowledgeable in Power BI storytelling capabilities to create data narratives that convey insights effectively and received consistently positive feedback from stakeholders on the clarity and impact of data stories
Well-versed in Agile project management methodologies, collaborating with cross-functional teams to deliver data solutions on time and within scope
Strong communication and interpersonal skills, capable of translating technical concepts to non-technical stakeholders, facilitating effective collaboration

Led the design, development, and maintenance of Azure Data Pipelines to ensure seamless and efficient movement of data from diverse source systems to data lakes and data warehouses
Created and optimized ETL processes using Azure Data Factory, Azure Databricks, and PySpark to transform raw data into structured formats, guaranteeing data quality and consistency while optimizing performance
Managed Azure Synapse Analytics to construct and maintain data warehouses, focusing on optimizing query performance, reducing data retrieval times, and ensuring scalability for analytics workloads
Utilized Azure Blob Storage and Azure Data Lake Storage for storing and managing structured and unstructured data, implementing strategies such as data partitioning and lifecycle management to optimize storage efficiency
Oversee complex data integration workflows using Azure Data Factory, Azure Logic Apps, and Azure Functions, ensuring smooth data flow between various systems and services
Managed Snowflake data warehouse configurations, including resource monitoring, scaling, and fine-tuning, to maintain optimal performance and cost efficiency
Demonstrated expertise in troubleshooting data engineering and ETL issues, rapidly identifying root causes, and implementing effective solutions to minimize downtime and data loss
Implemented data governance policies and security measures to ensure compliance with industry standards and regulations, including data encryption, access control, and auditing, to protect sensitive data
Monitored and optimized data pipelines and data warehouses for performance bottlenecks, troubleshooting issues, and implementing proactive monitoring solutions using Azure Monitor and other tools
Stay updated with the latest trends in data analysis, machine learning, and data technologies to expand skills and knowledge in the field of data analytics, contributing to ongoing professional growth and innovation

Extracted data from various source systems, including databases, flat files, APIs, and other data repositories, ensuring accuracy, completeness, and timeliness of data acquisition
Transformed raw data into structured, cleansed, and standardized formats using ETL tools such as Informatica, Talend, or SSIS (SQL Server Integration Services), applying business rules, data validation, and enrichment as required
Designed, developed, and implemented ETL processes and workflows to automate data movement and transformation tasks, optimizing performance and efficiency while adhering to best practices and standards
Integrated data from multiple sources into target systems such as data warehouses, data lakes, or operational databases, ensuring seamless data flow and compatibility across systems
Monitored and optimized ETL jobs for performance bottlenecks, identifying opportunities for optimization, fine-tuning queries, and improving overall ETL process efficiency to meet performance and scalability requirements
Implemented error-handling mechanisms and logging frameworks to capture and manage errors, exceptions, and data anomalies during the ETL process, ensuring data integrity and reliability
Implemented data quality checks, validation rules, and data profiling techniques to ensure the accuracy, completeness, and consistency of data throughout the ETL pipeline, addressing data quality issues as they arise