4529092-Lead Assistant Manager

Job Title: Senior Data Engineer Job Summary: We are looking for an experienced Senior Data Engineer with 5+ years of hands-on experience in cloud data engineering platforms, specifically AWS, Databricks, and Azure. The ideal candidate will play a critical role in designing, building, and maintaining scalable data pipelines and infrastructure to support our analytics and business intelligence initiatives. Key Responsibilities: Design, develop, and optimize scalable data pipelines using AWS services (e.g., S3, Glue, Redshift, Lambda). Build and maintain ETL/ELT workflows leveraging Databricks and Apache Spark for processing large datasets. Work extensively with Azure data services such as Azure Data Lake, Azure Synapse, Azure Data Factory, and Azure Databricks. Collaborate with data scientists, analysts, and stakeholders to understand data requirements and deliver high-quality data solutions. Ensure data quality, reliability, and security across multiple cloud platforms. Monitor and troubleshoot data pipelines, implement performance tuning, and optimize resource usage. Implement best practices for data governance, metadata management, and documentation. Stay current with emerging cloud data technologies and industry trends to recommend improvements. Required Qualifications: 5+ years of experience in data engineering with strong expertise in AWS , Databricks , and Azure cloud platforms. Hands-on experience with big data processing frameworks, particularly Apache Spark. Proficient in building complex ETL/ELT pipelines and managing data workflows. Strong programming skills in Python, Scala, or Java. Experience working with structured and unstructured data in cloud storage solutions. Knowledge of SQL and experience with relational and NoSQL databases. Familiarity with CI/CD pipelines and DevOps practices in cloud environments. Strong analytical and problem-solving skills with an ability to work independently and in teams. Preferred Skills: Experience with containerization and orchestration tools (Docker, Kubernetes). Familiarity with machine learning pipelines and tools. Knowledge of data modeling, data warehousing, and analytics architecture.