KARTIK TARAR

Project: Media Lift

• Worked as a Data Engineer on the Media Lift project, enabling customers to measure and quantify the impact of marketing campaigns by analyzing exposure, sales, and performance data across multiple channels.
• Designed, developed, and maintained end-to-end data pipelines using Python, SQL, and PySpark, handling large-scale datasets for campaign attribution, lift calculation, and performance analytics.
• Contributed to the lift_dataprep repository, which served as the core data processing layer, responsible for data ingestion, cleansing, transformation, aggregation, and business rule application to generate high-quality analytical datasets.
• Developed and maintained the lift_exposure repository, focused on processing and validating media exposure data, ensuring accurate mapping of customer exposure events to campaign timelines and downstream lift analysis.
• Worked extensively with Apache Airflow via the media_dag repository to orchestrate workflows, manage dependencies, schedule batch jobs, and ensure reliable and timely pipeline execution across environments.
• Implemented CI/CD pipelines using Jenkins, enabling automated build, test, and deployment of data pipelines, improving deployment reliability and reducing manual intervention.
• Used Git and GitHub for version control, code reviews, branching strategies, and collaboration across distributed teams, ensuring maintainable and auditable codebases.
• Partnered with analytics and business stakeholders to validate outputs, troubleshoot data issues, and ensure the pipelines delivered accurate lift metrics and actionable insights to customers.

• Led the optimisation of service request and incident resolution workflows within IT Operations by analysing high-volume support tickets and configuration data using PySpark on Databricks.
• Developed and deployed real-time operational dashboards for ITSM metrics (e.g., incident volume, resolution time, and change success rate) using Dash (Python), SQL, and Apache Airflow. These dashboards provided IT managers and service owners with real-time visibility into operations, significantly improving data-driven decision-making and governance.
  
• Built scalable data pipelines for CMDB enrichment and change-impact analysis using Databricks, Apache Spark, and Azure Data Lake, leveraging a Medallion Architecture (Bronze, Silver, Gold) to ensure high data quality, auditability, and efficient ETL, leading to a 20% improvement in change risk assessment performance.

• Designed and deployed interactive dashboards in Yellowfin BI, enabling clinical and administrative teams to monitor 20+ key operational and clinical metrics, supporting KPI and KRI alignment across departments.
• Developed SQL-based queries and reporting layers over large-scale healthcare datasets (EHR, billing, logistics) to deliver actionable BI insights, reducing inefficiencies by 11%.
• Improved reporting performance by optimizing SQL queries and ETL jobs, ensuring 90% near real-time availability of clinical and financial data for decision-making.
• Partnered with program managers and cross-functional teams to translate business needs into BI solutions, supporting compliance reporting and quarterly performance reviews.