Around 4 years of experience in Data Engineering, involved in the design, construction, and maintenance of data that support the storage, processing, and analysis of data. Proficient in building distributed data solution…
Around 4 years of experience in Data Engineering, involved in the design, construction, and maintenance of data that support the storage, processing, and analysis of data. Proficient in building distributed data solutions, data analytical applications, predictive modeling, and ETL/ELT and streaming pipelines leveraging big data technologies. Experienced in utilizing Hadoop ecosystem components, Databricks platform, and AWS services for developing data solutions.
, I honed my skills in building distributed data solutions and analytical applications leveraging big data technologies such as Hadoop ecosystem components, Databricks platform, and AWS services. I have hands-on experience in deploying data pipelines on Azure using Azure Data Factory and Azure Logic Apps, which have enabled seamless data integration and automation.
My proficiency in Dimensional Data Modeling, coupled with my expertise in in-memory data processing with Apache Spark using Scala and Python, has allowed me to optimize data processes and enhance operational efficiency. I have also demonstrated my ability to collaborate effectively with cross-functional teams, manage ETL pipelines, and develop and maintain dashboards for sales data reporting using Power BI.
Implemented data ingestion and processing solutions using Big Data technologies, handling over 2 million data records monthly with technologies like Hadoop, MapReduce, HBase, Hive, and Sqoop.
Transformed over 100 complex Hive/SQL queries into Spark transformations monthly using Spark RDD, Scala, and Python, achieving a 25% increase in data processing efficiency.
Enhanced data infrastructure and pipelines, increasing data processing efficiency.
Managed and processed data from Azure Data Lake Storage into Spark Data Frames monthly, performing vital data transformations and actions.
Demonstrated proficiency in Microsoft Azure Services, managing data across services like Azure VM, Azure Blob Storage, Azure SQL Database, Azure Synapse Analytics, Azure Data Factory, HDInsight, and Power BI.
Enhanced data processing with Hive by implementing partitioning and bucketing, generating data cubes monthly for advanced data visualization.
Worked with data engineering teams to improve data workflows, leading to an improvement in data quality and a reduction in data integration issues.
Regularly resolved data discrepancies for business users by optimizing complex SQL queries, ensuring data accuracy and reliability.
Developed Azure Functions to aggregate data from numerous event hubs, storing processed data in Azure Cosmos DB.
Created and maintained dashboards in Power BI for sales data reporting, enhancing data visualization and business insights.
Collaborated closely with senior management and IT teams to define business requirements, impacting data-driven projects.
Engineered ETL pipelines on Azure Databricks for efficient data ingestion, processing data from various sources each month.
Worked closely with data analysts to understand their reporting and analysis requirements and implement DBT models to transform raw data into structured, business-ready datasets.
Applied Data Modeling techniques in data warehousing, particularly snowflake schemas, managing models for databases.
Scheduled and debugged Azure Data Factory pipelines, ensuring smooth data processing workflows.
Engaged in meetings, leading discussions on projects handling datasets, and driving team alignment and project success.
Working on multiple projects for PoC (Proof of Concept), conducting a feasibility study to build & test key features developed before industrialization.
Predictive analytics for in-vehicle systems and involved in building datasets using advanced Python libraries.
Analysis and maintenance of large datasets using SQL to extract, transform, and load data for various analytical projects.
Performed statistical modeling and machine learning using Python, improving data accuracy and decision-making in predictive models.
Conducted exploratory data analysis to identify trends, anomalies, and insights, which contributed to informed business decisions.
Implemented data pipelines using Python to support product analytics.
Ensured compliance and governance by implementing data warehouse best practices and version control using GitHub for the enterprise data platform.
Collaborated with cross-functional teams in an Agile environment using Jira for project management and communication, contributing to the development of a scalable data architecture.
Utilized Snowflake to perform test automation and analysis, resulting in improved efficiency and accuracy in clinical trial data processing.
Complete execution of the project, including system (software & hardware) enhancements, upgrades & migrations, while ensuring minimal disruptions to ongoing operations.
Involved in rapid prototyping of Microautobox to replace the vehicle ECU for guidance and control advancement of an active system in the vehicle.
Designed and developed the state flow logic & control using Dspace ControlDesk, MATLAB, & CAN architecture to enable communication.
Conducted preliminary data analysis and prepared reports to support decision-making.
Leveraged SQL to query, extract, and manipulate large datasets, enabling in-depth analysis.
Utilized data modeling and analytics to identify customer purchase patterns and performed impact analysis on experiments, providing actionable recommendations to shape business campaigns, demonstrating strong analytical skills.
Utilized AWS services for data processing and storage, optimizing data workflows and enhancing data reliability and scalability.
Developed a data dashboard using Power BI to visualize customer complaints and resolutions at various granularities, showcasing data architecture and product analytics expertise.
Collaborated with senior data analysts to create data visualizations and assist in generating actionable insights.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
