MOHAMED KAVUNGAL

• Managing 100M+ daily user tracking events, developing high-performance, scalable ETL pipelines using Apache Spark (Scala Spark/SQL) and Databricks.

• Developed a new user session definition pipeline to process both historical and incremental data, ensuring consistency in user journey tracking.

• Optimised data transformation workflows, reducing query execution time from 12 minutes to just 29 seconds, significantly improving performance and cost efficiency.

• Designed and implemented Data Governance & Observability frameworks, improving data ownership, quality, and transparency.

• Collaborated with product, analytics, and engineering teams to develop data-driven insights, ensuring data accuracy and reliability.

• Designed and built batch data pipelines in AWS, enabling efficient, scalable data processing.

• Developed a Data Lakehouse using AWS & Apache Hudi, optimizing data workflows and addressing large-scale data management challenges.

• Introduced Airflow for orchestration, improving the efficiency and reliability of data pipelines.

• Played a key role in shaping the company’s data platform strategy, collaborating with cross-functional teams to deliver impactful data products.

• Led data engineering initiatives for multiple UK clients, designing and developing data pipelines on Google Cloud Platform (GCP).

• Provided technical leadership to a team of engineers, offering mentorship and guidance.

• Designed and implemented scalable data solutions, ensuring optimal data quality, security, and governance.

• Worked onsite with chief architects & stakeholders, shaping the design and architecture of next-generation data platforms.

• Developed and optimized batch data pipelines using Apache Spark, Hadoop, Hive, and Scala for large-scale data processing.

• Designed and implemented ETL workflows to ingest, transform, and store structured and semi-structured data in a centralized data lake.

• Optimized Spark jobs to improve performance, reducing execution time and resource consumption.

• Ensured data quality through deduplication, standardization, and enrichment for downstream analytics.

• Collaborated with cross-functional teams to develop scalable data models and reporting solutions.

• Responsible for creating and optimizing data processing pipelines using Hortonworks, Hadoop, Spark, Hive, and Scala
• Focused on designing and implementing efficient batch processing solutions to handle large volumes of data
• Employed Spark and Scala to build transformation logic for ETL tasks
• Ensured scalability and performance-oriented data processing frameworks within the Hadoop ecosystem
• Played a key role in ensuring accurate data processing to meet business needs and performance expectations

• Focused on core Java development, primarily working on standalone application projects to ensure a seamless and intuitive user experience
• Wrote efficient, maintainable code while debugging and troubleshooting issues
• Collaborated with cross-functional teams to gather requirements and deliver high-quality software solutions that met client specifications
• Adhered to best practices in software development, including version control and documentation, to facilitate team collaboration and project transparency