Job Summary The Senior Data Engineer will design, develop, and optimize enterprise-scale data engineering solutions, modern data platforms, and analytics architectures. The role requires deep hands-on expertise in AWS, PySpark, SQL, Python, dbt, Snowflake, and modern data technologies. The engineer will provide technical leadership across complex initiatives, translate business requirements into scalable solutions, and apply an AI-first engineering mindset to leverage emerging technologies, automation, and AI-assisted development practices to improve delivery, quality, and productivity. Key Responsibilities • Design, develop, and support enterprise-scale data engineering solutions and scalable data pipelines. • Build and optimize large-scale data processing solutions using SQL, Python, PySpark, and Spark. • Develop and maintain modern data platform solutions using Snowflake, dbt, Kafka, Airflow, Iceberg, and Lakehouse architectures. • Design and implement cloud-native data solutions using AWS services such as EMR, Glue, Athena, Lambda, DynamoDB, EC2, IAM, CloudFormation, and CloudWatch. • Apply data architecture and modeling principles, including dimensional modeling, Data Vault, data warehousing, data governance, and master data management. • Design and implement data streaming and distributed data processing solutions. • Evaluate technical approaches and clearly communicate architecture decisions, implementation strategies, challenges, and outcomes. • Lead technical initiatives and collaborate with engineering teams to deliver scalable data solutions. • Gather business requirements and translate complex business needs into technical designs and solutions. • Collaborate with business stakeholders and technical teams throughout the development lifecycle. • Apply AI-assisted development tools, automation frameworks, and emerging technologies to improve engineering productivity, software quality, and delivery. • Continuously evaluate and adopt modern engineering practices and technologies that improve data platform capabilities. • Support CI/CD and DevOps practices for data engineering solutions. • Troubleshoot performance, reliability, and data quality issues across data pipelines and platforms. Required Qualifications • 5-8 years of hands-on experience designing, developing, and supporting enterprise-scale data engineering solutions. • Expert-level proficiency in SQL, Python, and PySpark. • Strong experience building and optimizing large-scale data pipelines. • Hands-on experience with AWS cloud services and cloud-native data engineering solutions. • Strong experience with modern data technologies, including Spark, Snowflake, dbt, Kafka, Airflow, and Iceberg or Lakehouse architectures. • Strong understanding of data architecture and modeling concepts, including dimensional modeling, Data Vault, data warehousing, data governance, and master data management. • Experience with AWS services including EMR, Glue, Athena, Lambda, DynamoDB, EC2, IAM, CloudFormation, and CloudWatch. • Ability to explain technical designs, implementation decisions, challenges, and outcomes using concrete project examples. • Experience leading technical initiatives or teams. • Experience gathering business requirements and translating them into scalable technical solutions. • Strong stakeholder management and communication skills. • Demonstrated AI-first engineering mindset with experience evaluating and applying AI-assisted development tools, automation frameworks, and emerging technologies. • Strong problem-solving, analytical, and troubleshooting skills. Preferred Qualifications • Experience with CI/CD pipelines, GitLab, CodeCommit, and DevOps best practices. • Strong Linux administration and scripting skills using Bash/Shell and Python. • Experience with relational, NoSQL, and graph database technologies. • Knowledge of networking fundamentals, virtualization, and distributed systems. • Experience working in Agile environments using Jira or similar tools. • Experience collaborating with distributed and remote teams. • AWS Solutions Architect, AWS Developer, or AWS Data Analytics certification. • Bachelor's degree in Computer Science, Engineering, or a related technical field. • Master's degree in a related technical field.