Data Engineering Lead

As a Data Engineering Lead specializing in AI for Data Quality & Analytics with 7 to 10 years of experience, you will play a crucial role in developing and maintaining high-quality data ingestion and validation processes from various upstream systems. Your responsibilities will encompass designing and implementing scalable data quality validation systems, developing AI-driven tools for anomaly detection, and leading the development of data pipelines and validation scripts. Additionally, you will collaborate with stakeholders to proactively address reporting gaps and ensure auditability of decisions derived from enriched data. Your expertise in Python, Alteryx, SQL, and cloud data platforms like Snowflake will be essential for this role, along with a deep understanding of data pipelines, ETL/ELT processes, and data validation best practices. Experience with AI/ML in data quality and familiarity with enterprise systems like Workday, Beeline, and Excel-based reporting are also required. Strong interpersonal and communication skills are necessary to collaborate effectively with executive stakeholders and distributed teams. In this position, you will have the opportunity to lead a small, distributed team, mentoring junior engineers and analysts while optimizing headcount through AI augmentation. Your leadership will enable team members to focus on higher-value initiatives and align system architecture with business needs. Preferred attributes include experience in leading data modernization or AI transformation projects and exposure to dashboard adoption challenges and enterprise change management. If you are a data engineering professional with a passion for data quality and analytics, and possess the requisite skills and experience, we invite you to send your updated resume to swetha.p@zettamine.com. Join our team in Bangalore and contribute to building AI-enhanced quality frameworks, scalable reporting solutions, and automated anomaly detection systems that drive business insights and decision-making processes.,