Mohammad Parvez

About Mohammad Parvez

With over 12+ years of experience in Data Analyst, Data Engineering, Data Migration, and Data Warehousing. Specializing in ETL Design and Implementation, Data Governance, Enterprise Architecture, Data Governance, Data lake, Metadata management, cloud platforms, and big data analytics. Proficient in developing data analysis programs using Python and PySpark on cloud ecosystems and experienced in Spark SQL for data transformation in Databricks. Expertise in various databases and advanced analytical applications with Spark, and SQL/Oracle/Snowflake. Skilled in AWS with a strong focus on data extraction, transformation, and loading using S3 and Spark SQL.In my current company, I designed and optimized data pipelines that transform complex datasets into actionable insights by migrating on-premises systems to cloud-based solutions, enhancing data management and financial operations. Leading cross-functional teams and implementing innovative solutions to address large-scale data challenges while ensuring regulatory compliance.I achieved project objectives through requirements gathering, project management, and stakeholder management, ensuring data projects align with organizational goals and provide measurable results. My goal is to leverage my data engineering and analytics expertise to help organizations achieve their objectives and make a positive impact.Perpetually pursuing new challenges to expand my knowledge and expertise in the field of data engineering. If you\'re looking for a skilled and motivated data engineer, please don\'t hesitate to connect with me. I am excited about the opportunity to leverage my skills and experiences to contribute to your team\'s success.

Experience

1. Principal Data Engineer

   Legacy Mutual Mortgage

   Oct 2023 — Present

   Implemented various AWS cloud services for data storage, cleaning, transformation, and warehousing, resulting in a 30% reduction in manual work.• Leveraged Spark for large-scale data processing and analytics within the Apache Spark ecosystem, utilizing Data Frames and Spark SQL, and achieved 20% reduction in data processing time.• Created Airflow DAGs (Directed Acyclic Graphs) to schedule and monitor data processing tasks across various applications, accelerated deployment process by 30%.• Refactored existing on-premises applications to AWS by designing scalable data pipelines and developed plans to right-size data infrastructure, and decreased recurring costs by at least 40%

Education

• American International University

  Bachelor's degree, Computer Science