Asim Kareem Mohammad

•Designed and optimized Azure Synapse pipelines, SQL pools, and data flows supporting national-scale insurance ingestion and analytics workloads.
• Built enterprise ETL/ELT frameworks integrating insurer, broker, and regulatory datasets aligned with IBC P&C reporting standards.
• Implemented data governance, including validation rules, lineage, metadata, and privacy-compliant access controls.
•

• Designed cloud-native data platforms using AWS Redshift, Snowflake, EMR, S3, and Databricks to support analytics, ML, and real-time workloads.
• Built and optimized Airflow, Prefect, and dbt pipelines, improving data lineage, processing speed, and pipeline reliability.
• Engineered Databricks Lakehouse solutions using Delta Lake, Unity Catalog, and PySpark for batch, streaming, and ML feature

• Designed end-to-end data engineering solutions across requirements, architecture, development, testing, and deployment.
• Built real-time streaming applications using PySpark, Flink, Kafka, Hive, and Hadoop ecosystems.
• Developed Spark ML/MLlib workloads for anomaly detection and predictive analytics.
• Engineered Flink pipelines applying complex business logic and serializing outputs using

• Designed large-scale ingestion pipelines across Hadoop and cloud platforms.
• Built Spark jobs automating Hive metadata creation and improving data discoverability.
• Developed Python-based ingestion workflows using Apache NiFi for real-time and batch processing.
• Engineered Kafka-driven ingestion into HDFS and PostgreSQL, and built REST APIs supporting downstream reporting and analytics.
•

Relevant Coursework: Data Structures & Algorithms, Database Management Systems, Operating Systems, Software Engineering, Object-Oriented Programming, Data Mining, Computer Networks.