fawad maqbool

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

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

<p>Led the establishment and implementation of the data infrastructure for Sunfire GmbH Company, a startup firm, with a focus on renewable energies.<br>
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.<br>
Spearheaded proof-of-concept (POC) initiatives, experimenting with various technologies and methodologies to identify the most suitable solutions for data processing, storage, and analytics.<br>
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.<br>
Orchestrated the implementation and integration of chosen technologies, collaborating closely with cross-functional teams, including software engineers, data scientists, and business analysts.<br>
Established the cloud infrastructure on Microsoft Azure, leveraging its extensive suite of services for data storage, processing, and analysis.<br>
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.<br>
Developed robust data governance frameworks, ensuring compliance with industry regulations (e.g., GDPR) and data security best practices.<br>
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.<br>
Collaborated with the IT department to establish appropriate access controls and user permissions, ensuring data access and security align with business requirements.<br>
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.<br>
Conducted tuning and optimization activities, regular performance fine-tuning data processing workflows and infrastructure to enhance efficiency and reduce costs.<br>
Mentored and provided technical guidance to junior data engineers, fostering their professional growth and ensuring consistent quality standards across data engineering projects.<br>
Successfully architected and implemented a robust, scalable, and cost-effective data infrastructure, resulting in improved data accessibility, accuracy, and reliability.<br>
Led the migration of legacy on-premises data systems to the cloud, resulting in enhanced scalability, agility, and cost savings for the company.<br>
Established efficient data pipelines, reducing data processing time by 30% and enabling real-time data analytics capabilities.<br>
Implemented data governance and security measures, ensuring compliance with data privacy regulations and enhancing data protection for sensitive information.<br>
Created a unified data repository, enabling stakeholders to access reliable and consistent data, resulting in improved decision-making processes across the organization.</p>