AWS Data Engineering

Diverse Lynx is looking for AWS Data Engineering to join our dynamic team and embark on a rewarding career journey. Data Architecture Design : Designing and implementing scalable, distributed, and high - performance data architectures using AWS services like Amazon S3, Amazon Redshift, Amazon EMR, AWS Glue, and others. Data Processing and ETL : Building and managing Extract, Transform, Load (ETL) pipelines to collect, process, and transform large volumes of data using AWS tools like AWS Glue, Apache Spark, or custom scripts. Data Warehousing : Developing and managing data warehouse solutions using AWS Redshift or other relevant technologies to enable efficient data storage and analytics. Data Governance and Security : Implementing data governance frameworks, access controls, encryption, and compliance standards to ensure data security and regulatory compliance. Data Visualization and Analytics : Integrating data with analytics and visualization tools like AWS QuickSight, Tableau, or Power BI to derive insights and support decision - making processes. Performance Optimization : Optimizing data pipelines, storage, and querying performance for enhanced efficiency and cost - effectiveness. Qualifications and Skills : AWS Expertise : Proficiency in various AWS data services like S3, Redshift, Glue, EMR, Lambda, Kinesis, and related AWS data analytics and storage solutions. Data Engineering Skills : Strong understanding of data modeling, ETL processes, data warehousing, data lakes, and big data technologies. Programming and Scripting : Proficiency in programming languages like Python, SQL, Scala, or Java, as well as scripting for data processing tasks. Data Visualization Tools : Familiarity with tools for data visualization and analytics like QuickSight, Tableau, or Power BI. Key Attributes : Analytical Thinking : Ability to analyze complex data sets, identify patterns, and derive actionable insights. Problem - Solving Skills : Capability to troubleshoot data - related issues and optimize data pipelines for efficiency. Team Collaboration : Effective communication and collaboration with cross - functional teams, data scientists, analysts, and stakeholders.