Data Engineer (Data Analyst)

•Designed and implemented large-scale data pipelines and batch processing systems using Scala, Spark, and Hadoop for scalable data operations.
•Worked with distributed data processing systems to ensure efficient data management and faster processing times.
•Customized SSRS report output formats based on user preferences to enhance user experience and reporting flexibility.
•Utilized Azure Event Grid for real-time event processing, enabling instant data updates and reducing latency.
•Developed Power BI reports with dynamic visuals and slicers, automating reporting processes and reducing manual data handling hours.
•Collaborated on building fault-tolerant data processing systems to handle distributed datasets.
•Automated Databricks workspace deployments using Infrastructure as Code (IaC) tools like ARM and Terraform, streamlining development workflows.
•Managed SSAS perspectives for user-specific views and multilingual reporting, improving data accessibility for global teams.
•Automated data ingestion and processing workflows using cloud technologies, ensuring seamless integration and scalability.
•Designed and implemented caching strategies to improve query performance, reducing load times for end users.

•Architected and implemented a cloud-based data integration framework leveraging Azure and GCP, improving data synchronization and availability across platforms.
•Designed and implemented event-driven architectures and distributed data processing systems to handle large-scale data operations efficiently.
•Managed data synchronization within Azure Data Factory to streamline data workflows.
•Collaborated with executive leadership to develop a centralized data warehouse strategy, improving reporting accuracy and decision-making.
•Optimized Power BI reports by implementing advanced analytics (forecasting, clustering, sentiment analysis) and enhancing performance for faster load times.
•Integrated data lakes with Azure Functions for server less data processing, reducing infrastructure complexity.
•Used Python to extract and transform data from APIs, databases, and files.
•Created dynamic SSRS report subscriptions for targeted delivery, enhancing reporting efficiency.
•Designed and deployed data processing systems on Azure, utilizing tools like Databricks, Spark, and SQL for high-volume data operations.
•Conducted data cleansing and normalization to ensure consistency and accuracy across datasets.
•Implemented predictive modeling solutions to improve user targeting and drive ROI.
•Led A/B testing to validate ad relevance strategies, optimizing campaign performance.
• Developed Spark-based data pipelines using Scala for large-scale distributed processing.

•Designed and optimized scalable ETL pipelines in Snowflake, leveraging Snow park for custom data transformations and improving system scalability.
•Developed data processing solutions using Spark and other distributed computing technologies for large-scale datasets.
•Managed cross-functional collaborations with data science and analytics teams to develop a centralized data platform, streamlining access to critical business insights.
•Collaborated with leadership teams to define and refine data integration strategies, ensuring alignment with business objectives.
•Utilized AWS services for data storage and processing, integrating AWS Glue and S3 to manage large-scale ETL pipelines.
•Collaborated with Azure Purview’s AI to implement predictive data quality management solutions.
•Utilized Python for data cleaning and transformation to ensure data integrity.
•Developed CI/CD pipelines using Git for automated build, testing, and deployment.
•Defined and refined data integration strategies in alignment with business objectives, leveraging Snowflake for secure data sharing and scalable data integration.
•Implemented Azure Synapse user-defined roles and RBAC policies to streamline data access and security.
•Configured Power BI workspaces for efficient collaboration and reporting across teams.
•Managed Azure Data Factory pipeline monitoring and alerts to ensure operational efficiency.
•Implemented Power BI data security measures and compliance standards to protect sensitive data.
•Developed scalable data models supporting business growth, automating processes to reduce manual errors and improve operational efficiency, in alignment with Gorgias’ vision for continuous improvement.
•Built predictive models to forecast customer behavior, optimizing ad performance and relevance in collaboration with product teams.
•Worked with data lakes, data warehouses, and distributed systems to optimize query performance, enabling faster reporting and accurate insights.
• Migrated terabyte-scale data from legacy systems to Databricks, enhancing query performance and system scalability. Integrated data health metrics and observability tools to enhance governance and data reliability.
• Developed and enforced data governance policies, integrating security measures to ensure data integrity, compliance, and protection of sensitive customer information per industry regulations.