Data Engineering Practice Head

As a Data and AI initiatives driver, your main focus will be on revenue recognition and profitability management. You will be responsible for leading a high-performing team, nurturing client and partner relationships, owning the practice's solutioning team, and handling RFP responses. Key Responsibilities: - Provide expert guidance in data engineering including architecture, warehousing, ETL/ELT, data lakes, pipelines, and governance. - Stay updated with AI/ML, big data, and cloud platforms such as AWS, Azure, GCP. - Oversee the design and implementation of scalable and secure data solutions. Qualifications Required: - Minimum 20 years of total experience, ideally transitioning from a data career into a data engineering leadership role. - Design and management experience with large-scale modern architecture data platforms. - Strong understanding of AI/ML concepts and their business applications. - Extensive hands-on experience with cloud data platforms like AWS, Azure, and GCP. - Interest and exposure to emerging AI technologies, including Agentic AI and advanced automation. - Proficiency in Spark, Databricks, databases (SQL, NoSQL), data warehousing (Snowflake, Redshift, Big Query), ETL/ELT, and data quality and governance. No additional details about the company were provided in the job description. As a Data and AI initiatives driver, your main focus will be on revenue recognition and profitability management. You will be responsible for leading a high-performing team, nurturing client and partner relationships, owning the practice's solutioning team, and handling RFP responses. Key Responsibilities: - Provide expert guidance in data engineering including architecture, warehousing, ETL/ELT, data lakes, pipelines, and governance. - Stay updated with AI/ML, big data, and cloud platforms such as AWS, Azure, GCP. - Oversee the design and implementation of scalable and secure data solutions. Qualifications Required: - Minimum 20 years of total experience, ideally transitioning from a data career into a data engineering leadership role. - Design and management experience with large-scale modern architecture data platforms. - Strong understanding of AI/ML concepts and their business applications. - Extensive hands-on experience with cloud data platforms like AWS, Azure, and GCP. - Interest and exposure to emerging AI technologies, including Agentic AI and advanced automation. - Proficiency in Spark, Databricks, databases (SQL, NoSQL), data warehousing (Snowflake, Redshift, Big Query), ETL/ELT, and data quality and governance. No additional details about the company were provided in the job description.