• Design, develop, and maintain scalable data pipelines using AWS services. • Build and optimize ETL/ELT workflows for structured and unstructured data. • Implement data lakes and data warehouses on AWS. • Work with large datasets to ensure high performance, reliability, and data integrity. • Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements. • Perform data modeling for analytical and operational use cases. • Ensure data quality, governance, and security best practices. • Monitor and troubleshoot data workflows and production issues. • Support CI/CD and automation for data platform deployments.

• Lead the design and implementation of large-scale data architectures for cloud-based systems (AWS, Azure) to efficiently ingest, store, and process massive volumes of security telemetry and alerts • Spearhead advanced AI/ML initiatives, including Generative AI, to develop end-to-end AI solutions for SOC automation, threat detection, and threat hunting, leveraging frameworks like Scikit-learn, TensorFlow, and PyTorch • Drive the use of Large Language Models (LLMs) and AI Agents to enhance the enrichment of security data, enabling faster human decision-making, while exploring and evaluating various LLM architectures • Collaborate across teams to integrate ML-driven insights into the platform and apply automation and analytics to reduce analyst workload and enhance detection fidelity • Provide architectural guidance across engineering based on the fast-paced world of GenAI, Agents, and classic ML models, including those developed by our internal R&D teams

Job Summary: We are looking for a skilled Data Engineer with strong hands-on experience in AWS to design, build, and maintain scalable data pipelines and cloud-based data platforms. The ideal candidate will have expertise in modern data warehousing, ETL/ELT development, and distributed data processing while ensuring data quality, performance, and security. Key Responsibilities: ·      Design, develop, and maintain scalable data pipelines using AWS services. ·      Build and optimize ETL/ELT workflows for structured and unstructured data. ·      Implement data lakes and data warehouses on AWS. ·      Work with large datasets to ensure high performance, reliability, and data integrity. ·      Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements. ·      Perform data modeling for analytical and operational use cases. ·      Ensure data quality, governance, and security best practices. ·      Monitor and troubleshoot data workflows and production issues. ·      Support CI/CD and automation for data platform deployments. Required Skills & Experience: ·      Strong hands-on experience with AWS services such as S3, Glue, Redshift, Lambda, EMR, Athena, and RDS. ·      Proficiency in Python and SQL for data processing and analysis. ·      Experience in building and optimizing ETL/ELT pipelines. ·      Solid understanding of data warehousing and data lake architecture. ·      Experience with Apache Spark / PySpark. ·      Knowledge of workflow orchestration tools (Airflow or similar). ·      Familiarity with streaming frameworks (Kinesis/Kafka) is a plus. ·      Experience with data modeling and performance tuning. ·      Understanding of DevOps, CI/CD, and infrastructure as code (Terraform/CloudFormation). ·      Experience working in Agile/Scrum environments. Good to Have: ·      Experience with Snowflake on AWS. ·      Exposure to real-time data processing. ·      AWS certification (e.g., AWS Certified Data Analytics / Solutions Architect). Soft Skills: ·      Strong problem-solving and analytical skills. ·      Excellent communication and stakeholder management. ·      Ability to work in a fast-paced, collaborative environment.

Job Summary: We are looking for a skilled Data Engineer with strong hands-on experience in AWS to design, build, and maintain scalable data pipelines and cloud-based data platforms. The ideal candidate will have expertise in modern data warehousing, ETL/ELT development, and distributed data processing while ensuring data quality, performance, and security. Key Responsibilities: ·      Design, develop, and maintain scalable data pipelines using AWS services. ·      Build and optimize ETL/ELT workflows for structured and unstructured data. ·      Implement data lakes and data warehouses on AWS. ·      Work with large datasets to ensure high performance, reliability, and data integrity. ·      Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements. ·      Perform data modeling for analytical and operational use cases. ·      Ensure data quality, governance, and security best practices. ·      Monitor and troubleshoot data workflows and production issues. ·      Support CI/CD and automation for data platform deployments. Required Skills & Experience: ·      Strong hands-on experience with AWS services such as S3, Glue, Redshift, Lambda, EMR, Athena, and RDS. ·      Proficiency in Python and SQL for data processing and analysis. ·      Experience in building and optimizing ETL/ELT pipelines. ·      Solid understanding of data warehousing and data lake architecture. ·      Experience with Apache Spark / PySpark. ·      Knowledge of workflow orchestration tools (Airflow or similar). ·      Familiarity with streaming frameworks (Kinesis/Kafka) is a plus. ·      Experience with data modeling and performance tuning. ·      Understanding of DevOps, CI/CD, and infrastructure as code (Terraform/CloudFormation). ·      Experience working in Agile/Scrum environments. Good to Have: ·      Experience with Snowflake on AWS. ·      Exposure to real-time data processing. ·      AWS certification (e.g., AWS Certified Data Analytics / Solutions Architect). Soft Skills: ·      Strong problem-solving and analytical skills. ·      Excellent communication and stakeholder management. ·      Ability to work in a fast-paced, collaborative environment.