\\\\Introduction\\\\ A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe. You'll work with visionaries across multiple industries to modernize their data and cloud landscapes, accelerating adoption of hybrid cloud and AI-ready platforms. Your ability to drive meaningful change for clients is supported by our ecosystem of strategic partners and our technology platforms across the IBM portfolio; including Software and Red Hat. Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in measurable impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment that embraces your unique skills and experience. \\\\Your role and responsibilities\\\\ We are looking for motivated and tech-passionate individuals to: Lead the development and maintenance of data ingestion pipelines and data platform components on AWS Implement data lake architectures leveraging Amazon S3, AWS Glue, AWS Lake Formation and modern table formats (e.g., Iceberg) Operate batch, streaming, and change data capture (CDC) ingestion patterns using services such as Amazon DMS, Amazon Kinesis Data Streams and Kinesis Data Firehose Integrate data from SaaS platforms and enterprise systems (e.g., SAP Datasphere) using Amazon AppFlow Develop and optimize ETL/ELT transformations in Python, PySpark and SQL across Bronze/Silver/Gold data layers Manage data cataloging, schema evolution and permission models through AWS Glue Data Catalog and AWS Lake Formation Collaborate with architects and platform leads to troubleshoot complex issues, optimize performance and resource usage, and ensure secure data operations Document technical implementations, operational procedures and best practices to support delivery teams and stakeholders \\\\Required technical and professional expertise\\\\ Minimum 3-5 years of experience in data engineering and/or cloud data workloads Strong hands-on experience in AWS analytics services such as AWS Glue, AWS Lake Formation, and Amazon S3 Experience building ingestion pipelines using Amazon DMS (full load + CDC) Familiarity with streaming ingestion using Amazon Kinesis Data Streams and Kinesis Data Firehose Proficiency in ETL/ELT development using Python, PySpark and SQL Knowledge of modern data lake and lakehouse patterns including Iceberg, partitioning strategies, and data lifecycle management Experience implementing multi-layer data models (Bronze/Silver/Gold) Experience managing data cataloging and permission models via AWS Glue Data Catalog and Lake Formation Exposure to cloud security, IAM and cost-awareness in data workloads Interest in pursuing AWS certifications aligned to data engineering or analytics \\\\Preferred technical and professional experience\\\\ Agile mindset - willingness to learn, adapt to changing priorities, take initiative, and apply critical thinking One of the following: Experience integrating SaaS platforms or SAP systems via Amazon AppFlow or similar tools Familiarity with hybrid enterprise integration scenarios (e.g., SAP Datasphere) Experience with observability tooling for data platforms (logging, metrics, tracing) Exposure to containerization/orchestration (Docker, ECS, EKS, Kubernetes) IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

The role requires a minimum of 3-5 years of experience in data engineering and/or cloud data workloads. Strong hands-on experience with AWS analytics services such as AWS Glue, AWS Lake Formation, and Amazon S3 is necessary. Experience building ingestion pipelines using Amazon DMS, including full load and CDC, is also required. Proficiency in ETL/ELT development using Python, PySpark, and SQL is essential, along with knowledge of modern data lake and lakehouse patterns. Experience implementing multi-layer data models and managing data cataloging and permission models are key qualifications. An interest in pursuing AWS certifications aligned to data engineering or analytics is also mentioned.