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Role Overview: At EY, as a Manager II Data Engineering, you will have the opportunity to lead a team of data engineers to design, develop, and optimize scalable data pipelines and infrastructure. Your focus will be on enabling high-performance data workflows, ensuring seamless data integration, and driving automation to enhance data accessibility. Key Responsibilities: - Hire, mentor, and manage a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning in data engineering best practices. - Design, develop, and maintain scalable ETL (Extract Transform Load) pipelines to efficiently process and integrate structured and unstructured data from multiple sources. - Build and maintain scalable fault-tolerant data architectures ensuring efficient data storage retrieval and processing across cloud and on-premises environments. - Oversee the design, implementation, and performance tuning of relational and NoSQL databases ensuring data consistency, security, and accessibility. - Automate data ingestion, transformation, and validation processes to enhance data pipeline efficiency and reduce manual intervention. - Develop cloud-based data solutions using platforms like AWS, Azure, or Google Cloud leveraging technologies such as Spark, Hadoop, and Kafka for large-scale data processing. - Work closely with data scientists, analysts, and business intelligence teams to understand data requirements and develop scalable solutions for analytics and reporting. - Proactively monitor data pipelines and systems for performance issues, troubleshoot failures, and implement preventive measures for system reliability. - Partner with business and IT leaders to align data engineering initiatives with company goals, ensuring seamless integration of data-driven solutions across departments. Qualification Required: To qualify for the role, you must have: - Minimum 8 years of relevant work experience with at least 5 years in a leadership role managing data engineering teams and large-scale data infrastructure. - Bachelor's degree (B.E./) in Computer Science, IT, or a related field or Diploma in Data Science/Data Engineering. - Strong expertise in ETL development, data warehousing, and real-time data processing frameworks. - Hands-on experience with SQL, Python, Spark, Kafka, and cloud platforms (AWS, Azure, Google Cloud). - Proficiency in database technologies (PostgreSQL, MySQL, MongoDB, Snowflake, Big Query) and distributed computing frameworks. - Hands-on experience with Databricks including Spark and Delta Lake. - Strong Python, PySpark, and SQL skills beyond standard data engineering proficiency. - Experience building and maintaining scalable ETL and ELT pipelines specifically in AWS. - Understanding of Medallion Architecture and data validation best practices. - Familiarity with orchestration tools such as Airflow and Databricks Workflows. - Experience with Kimball-style dimensional modeling including star schemas. - Experience working with CI/CD pipelines and infrastructure-as-code tools such as Terraform. - Exposure to Delta Live Tables and Unity Catalog for data governance and pipeline automation. - Knowledge of DataOps practices and data quality frameworks. Additional Company Details: EY is a global leader in assurance, tax, transaction, and advisory services with a focus on utilizing technology to drive growth opportunities and solve complex business problems for clients. The organization supports technology needs through three business units: Client Technology, Enterprise Workplace Technology, and Information Security, all aimed at providing innovative solutions and ensuring data security and accessibility. EY's goal is to build a better working world by creating value for clients, people, and society through trust, diversity, and technology. Role Overview: At EY, as a Manager II Data Engineering, you will have the opportunity to lead a team of data engineers to design, develop, and optimize scalable data pipelines and infrastructure. Your focus will be on enabling high-performance data workflows, ensuring seamless data integration, and driving automation to enhance data accessibility. Key Responsibilities: - Hire, mentor, and manage a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning in data engineering best practices. - Design, develop, and maintain scalable ETL (Extract Transform Load) pipelines to efficiently process and integrate structured and unstructured data from multiple sources. - Build and maintain scalable fault-tolerant data architectures ensuring efficient data storage retrieval and processing across cloud and on-premises environments. - Oversee the design, implementation, and performance tuning of relational and NoSQL databases ensuring data consistency, security, and accessibility. - Automate data ingestion, transformation, and validation processes to enhance data pipeline efficiency and reduce manual intervention. - De
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