fawad maqbool

Led end-to-end data engineering projects, specializing in the Hadoop ecosystem, including Hive, Beeline, Hue, and other related technologies.
Oversaw the migration of on-premises Hadoop pipelines to the Azure cloud platform, ensuring seamless data flow and optimizing performance.
Collaborated with cross-functional teams to gather requirements, design data models, and develop robust data pipelines to support various business initiatives.
Implemented industry-standard practices, such as Delta Lake and Unity Catalogue, to enhance data management, quality, and governance in Databricks workflows.
Conducted performance tuning and optimization of Hive queries, improving query response times and overall system efficiency.
Developed and maintained ETL processes to extract, transform, and load large volumes of data from heterogeneous sources into the Hadoop ecosystem.
Created and executed data validation and quality assurance frameworks to ensure data accuracy, consistency, and integrity.
Designed and implemented data security measures, including encryption, access controls, and data masking, to comply with industry standards and regulations.
Collaborated with data scientists to operationalize machine learning models within the Hadoop ecosystem and ensure smooth integration with existing data pipelines.
Actively participated in code reviews, providing constructive feedback and promoting best practices for maintainable and scalable code.
Successfully led the migration of multiple Hadoop pipelines from on-premises infrastructure to Azure cloud, reducing infrastructure costs by 30% and improving scalability and availability.
Implemented Delta Lake and Unity Catalogue in Databricks workflows, resulting in improved data lineage, simplified metadata management, and accelerated development cycles.
Streamlined data ingestion processes by optimizing ETL pipelines, resulting in a 40% reduction in data processing time.
Identified and resolved performance bottlenecks in Hive queries, resulting in a 50% improvement in query response times.
Implemented data governance practices, ensuring compliance with data privacy regulations (e.g., GDPR) and enhancing data security and access controls.

Development/ Extension of mathematical models governing material response.
Implementation of constitutive material models for numerical simulations as user-subroutines in Fortran and MATLAB.
Automated post-processing of numerical simulations using Python and shell scripting.
Development of algorithms to automate material testing and machine learning models to replicate the material behavior.
Data-driven material modeling and parameter identification.
Management, coordination, and processing of public-funded research projects (Projects duration: 24 – 30 Months).

Led the establishment and implementation of the data infrastructure for Sunfire GmbH Company, a startup firm, with a focus on renewable energies.
Conducted thorough analysis of business requirements, collaborating with stakeholders to understand data needs and define key performance indicators (KPIs) to drive data-driven decision-making across the organization.
Spearheaded proof-of-concept (POC) initiatives, experimenting with various technologies and methodologies to identify the most suitable solutions for data processing, storage, and analytics.
Conducted comprehensive evaluations of different data platforms, such as Hadoop, Spark and various data ingestion, transformation and loading tools, based on performance, scalability, and cost-efficiency, ultimately guiding the selection of optimal technologies for the company's specific needs.
Orchestrated the implementation and integration of chosen technologies, collaborating closely with cross-functional teams, including software engineers, data scientists, and business analysts.
Established the cloud infrastructure on Microsoft Azure, leveraging its extensive suite of services for data storage, processing, and analysis.
Designed and implemented data pipelines, ensuring efficient extraction, transformation, and loading (ETL) processes to populate the data warehouse and data lakes while maintaining data quality and integrity.
Developed robust data governance frameworks, ensuring compliance with industry regulations (e.g., GDPR) and data security best practices.
Implemented data monitoring and alerting systems to proactively identify and resolve any issues or anomalies in data pipelines, ensuring the reliability and accuracy of the data infrastructure.
Collaborated with the IT department to establish appropriate access controls and user permissions, ensuring data access and security align with business requirements.
Collaborated with business stakeholders to define and establish a single source of truth for the company's data, creating unified and reliable data repositories that enable comprehensive reporting and analytics.
Conducted tuning and optimization activities, regular performance fine-tuning data processing workflows and infrastructure to enhance efficiency and reduce costs.
Mentored and provided technical guidance to junior data engineers, fostering their professional growth and ensuring consistent quality standards across data engineering projects.
Successfully architected and implemented a robust, scalable, and cost-effective data infrastructure, resulting in improved data accessibility, accuracy, and reliability.
Led the migration of legacy on-premises data systems to the cloud, resulting in enhanced scalability, agility, and cost savings for the company.
Established efficient data pipelines, reducing data processing time by 30% and enabling real-time data analytics capabilities.
Implemented data governance and security measures, ensuring compliance with data privacy regulations and enhancing data protection for sensitive information.
Created a unified data repository, enabling stakeholders to access reliable and consistent data, resulting in improved decision-making processes across the organization.