As a Lead Data Engineer/ETL Lead, your role will involve leading the design, development, and optimization of end-to-end data pipelines across batch and real-time processing. You will be responsible for architecting and implementing enterprise-grade ETL solutions using Informatica, Ab Initio, and cloud-native services. Your key responsibilities will include:
Driving large-scale data migration and conversion initiatives, including mock runs, reconciliation, validation, and production cutovers. - Designing and managing cloud-based data platforms, leveraging Snowflake and AWS analytics services. - Building and optimizing PySpark-based data processing frameworks for high-volume datasets. - Implementing real-time data ingestion and transformation pipelines using Kafka and streaming technologies. - Owning performance tuning, scalability, cost optimization, and SLA adherence for data workloads. - Collaborating closely with business, functional, and architecture teams to translate requirements into robust technical solutions. - Leading and mentoring development teams, conducting code reviews, and enforcing engineering best practices. - Overseeing CI/CD, scheduling, monitoring, and operational stability of data pipelines. - Supporting Agile delivery by participating in planning, backlog grooming, execution, and retrospectives. No additional details about the company were provided in the job description. As a Lead Data Engineer/ETL Lead, your role will involve leading the design, development, and optimization of end-to-end data pipelines across batch and real-time processing. You will be responsible for architecting and implementing enterprise-grade ETL solutions using Informatica, Ab Initio, and cloud-native services. Your key responsibilities will include:
Driving large-scale data migration and conversion initiatives, including mock runs, reconciliation, validation, and production cutovers. - Designing and managing cloud-based data platforms, leveraging Snowflake and AWS analytics services. - Building and optimizing PySpark-based data processing frameworks for high-volume datasets. - Implementing real-time data ingestion and transformation pipelines using Kafka and streaming technologies. - Owning performance tuning, scalability, cost optimization, and SLA adherence for data workloads. - Collaborating closely with business, functional, and architecture teams to translate requirements into robust technical solutions. - Leading and mentoring development teams, conducting code reviews, and enforcing engineering best practices. - Overseeing CI/CD, scheduling, monitoring, and operational stability of data pipelines. - Supporting Agile delivery by participating in planning, backlog grooming, execution, and retrospectives. No additional details about the company were provided in the job description.