Mohammad Al-Sarayreh
I am a results-driven Senior Data Engineer with over 6 years of experience designing and optimizing data lakehouse and warehouse solutions for critical domains such as legal research, aviation compliance, ERP/CRM…
About
I am a results-driven Senior Data Engineer with over 6 years of experience designing and optimizing data lakehouse and warehouse solutions for critical domains such as legal research, aviation compliance, ERP/CRM systems, and US government chemical taxation. I specialize in building scalable ETL/ELT pipelines, real-time streaming systems, and cloud-native data platforms that reduce costs, improve data quality, and accelerate decision-making. I have a proven track record of enabling diverse teams including attorneys, compliance officers, executives, and government agencies to work with accurate, secure, and audit-ready data.
Throughout my career, I have collaborated closely with legal, compliance, and research teams to prepare AI-ready datasets and establish strong data governance frameworks that comply with industry standards in privacy and security. I am passionate about leveraging modern cloud technologies and AI/ML integration to build efficient data solutions that drive business insights and operational excellence.
- 6 years' experience
- Joined August 2025
Industry experience
- Government
- Transportation & Logistics
- Financial Services
- Software & Internet
- Professional Services
Work experience· 6 roles
Designed and deployed ETL/ELT pipelines processing over 100 TB of legal and regulatory data monthly using Python, Apache Spark, and Apache Airflow. Built and maintained a data lakehouse architecture with Silver layers for standardized legal data and optimized a legal data warehouse in Amazon Redshift for business-ready models used by attorneys and compliance teams. Developed data acquisition workflows via web scraping, APIs, and databases to cover court documents and regulatory updates. Partnered with legal and compliance teams to prepare AI-ready datasets improving classification accuracy in legal case retrieval systems. Established data governance and validation frameworks meeting legal standards for privacy, confidentiality, and jurisdictional rules, enhancing security with encryption, IAM-based controls, and audit monitoring.
Designed and deployed ETL/ELT pipelines using Python, Apache Spark, and Apache Airflow processing over 100 TB of legal and regulatory data monthly. Built and maintained data lakehouse architecture with Silver layers and a Gold-layer data warehouse on Amazon Redshift for legal and compliance teams. Developed data acquisition workflows sourcing court documents and regulatory updates through web scraping, APIs, databases, and files. Collaborated with legal research and compliance teams to prepare AI-ready datasets, improving classification accuracy in legal case retrieval by reducing data preparation costs by 70%. Established data governance and validation frameworks to adhere to legal privacy and confidentiality standards and strengthened security through encryption, IAM controls, and audit monitoring.
Contributed to AI agent development enhancing decision support systems in industrial and government domains. Led data engineering for Valence, building a data warehouse tracking civil aircraft parts, maintenance, and repair workflows to ensure compliance with aviation safety. Designed and implemented pipelines for US government chemical taxation systems, delivering audit-ready analytics that improved federal tax compliance and reporting accuracy. Automated multi-source data ingestion for AI-driven applications reducing manual reporting workloads by 50%. Optimized real-time voice and text pipelines supporting compliance monitoring. Built data governance frameworks aligned with aviation and chemical taxation regulations to ensure audit readiness.
Education
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