Lead the development of a cutting-edge data platform that powers insights and innovation across a fast-paced, data-driven organization. If you're passionate about building robust, scalable pipelines and driving real business value through data, this is your role.You’ll scale a modern, cloud-native data ecosystem that supports petabyte-scale processing, real-time analytics, and data lake optimization using the latest open-source and AWS-native tools.What You’ll Tackle:
* Design and build scalable, reliable data pipelines using Spark, SQL, and Airflow
* Architect cloud-native data solutions on AWS leveraging services such as S3, Glue, Redshift, EMR, Athena, and Lambda
* Optimize batch and streaming data workflows for performance, cost-efficiency, and resilience
* Implement and manage metadata-driven lakehouse architectures using tools like Apache Iceberg or Delta Lake
* Establish data quality, governance, and access control best practices in complex multi-tenant environments
* Mentor junior engineers and shape technical strategy across the data platform
Experience/Skills:
* Proven expertise in large-scale data engineering, with hands-on experience designing and optimizing ETL/ELT pipelines
* Strong proficiency in SQL and Python (Scala or Java is a plus), with deep knowledge of Spark and Airflow
* Solid experience in the AWS data ecosystem, including services like Glue, Redshift, EMR, S3, and related analytics tools
* Familiarity with data lakehouse concepts and frameworks (Iceberg, Hudi, or Delta Lake)
* Practical experience with real-time and batch processing patterns using Kafka, Kinesis, or similar tools
* A collaborative mindset, strong communication skills, and a drive for technical excellence and ownership
Be part of a culture where autonomy is valued, engineering is celebrated, and data drives real outcomes. #J-18808-Ljbffr