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Kamal Jutt

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Texas, United States

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Résumé


Jobs verified_user 0% verified
  • Secoda
    Senior Data Engineer
    Secoda
    Dec 2022 - Current (3 years 11 months)
    • Led design and implementation of cloud-native data platforms (AWS, Azure) supporting real-time analytics and predictive insights, improving data accessibility by 60%. • Built scalable Kafka-based pipelines processing 2M+ messages per second, reducing latency by 40% and improving data reliability.
  • R
    Big Data Engineer
    Rill Data
    Aug 2018 - Nov 2022 (4 years 4 months)
    • Engineered high-volume big data pipelines using Apache Spark, Kafka, and Airflow, processing 10B+ records daily across distributed systems. • Optimized Snowflake and Redshift data warehouses, reducing BI query latency by 55% and improving reporting performance.
  • D
    Data Engineer, Narrator
    Jun 2015 - Jul 2018 (3 years 2 months)
    • Developed and maintained ETL pipelines for structured/unstructured data across Hadoop and SQL ecosystems, improving data delivery SLAs by 40%. • Built SQL-based data models, stored procedures, and views, enhancing analytics efficiency and supporting enterprise-wide BI dashboards.
Education verified_user 0% verified
  • B
    Bachelor of Science
    Feb 2011 - Current (15 years 9 months)
Projects (professional or personal) verified_user 0% verified
  • R
    Real-Time Data Streaming Platform Kafka + Spark + Snowflake
    Aug 2025 - Nov 2025 (4 months)
    • Architected and deployed a real-time data ingestion system handling 2M+ Kafka messages per second across distributed clusters. • Integrated Spark Structured Streaming with Snowflake for real-time analytics and alerting, cutting latency by 45%. • Achieved 99.9% message delivery reliability and reduced downstream lag through partition optimization and schema evolution. Spark, Snowflake, AWS Kinesis, Datadog
  • C
    Cloud Data Lake Modernization AWS & Databricks
    Feb 2025 - Apr 2025 (3 months)
    • Managed migration from on-prem Hadoop to AWS-based Databricks platform, improving data scalability and reducing storage cost by 35%. • Built data ingestion frameworks using AWS Glue and Delta Lake, automating schema tracking and lineage. • Planned cost-optimized storage tiering strategy using S3 and Redshift Spectrum for hybrid workloads. Tech Stack: Databricks, AWS S3, Redshift, Glue, Delta Lake, Python, Terraform
  • M
    Machine Learning Data Pipeline Automation
    Feb 2025 - Apr 2025 (3 months)
    • Developed ML data preparation pipelines supporting model training and feature store population in Snowflake and Databricks. • Enabled automate retraining triggers and versioned feature storage, improving model refresh efficiency by 60%. • Supported MLOps deployment via MLflow and SageMaker, integrating CI/CD pipelines for reproducible experiments. Snowflake, Databricks, MLflow, AWS SageMaker, Airflow,
  • S
    Snowflake
    May 2024 - Aug 2024 (4 months)
    Designed and optimized Snowflake data warehouses and ETL workflows.