Lead Data Engineer - AWS RedShift

As a Lead Data Engineer specializing in AWS RedShift, you will play a crucial role in designing, developing, and optimizing scalable ETL/ELT pipelines to facilitate efficient data integration for critical business intelligence and analytics initiatives. **Key Responsibilities:** - Lead the design and architecture of scalable data integration solutions both on-premise and in the cloud, ensuring alignment with business requirements and industry standards. - Engage with executive leadership and stakeholders to translate business needs into technical solutions. - Define data models and structures to optimize data storage, retrieval, and processing for analytical workloads. - Provide technical guidance, mentorship, and support to a team of data engineers. - Plan, prioritize, and manage data engineering projects, tasks, and timelines effectively. - Design, develop, and maintain data integration pipelines and ETL/ELT workflows. - Oversee multiple projects or manage larger teams with sub-tracks. **Qualifications Required:** - Proficiency in basic and advanced SQL. - Strong skills in Python for data integration and engineering tasks. - Experience with ETL/ELT tools such as Informatica, Talend, DataStage, SSIS, DBT, Databricks, or equivalent. - Familiarity with relational and NoSQL databases, cloud databases, and big data technologies. - Solid understanding of data modeling, warehousing concepts, and practices. - Knowledge of performance tuning, optimization, data profiling, and validation. - Experience in requirements gathering, documentation, and unit testing. - Aptitude for logical reasoning, analytical skills, and adaptability to an Agile environment. The company is seeking a Lead Data Engineer with a minimum of 8 years of experience in ETL/ELT, preferably Informatica IICS/Powercenter, and at least 4 years of hands-on experience with AWS RedShift. Strong proficiency in AWS Redshift, complex SQL query writing, Python programming, version control tools like Git, CI/CD workflows, data modeling, and warehousing concepts are essential. Prior exposure to data lakes and big data technologies will be advantageous. Relevant certifications in data engineering or cloud platforms are considered a plus.,