Introduction
At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk.

Your Role and Responsibilities
Develops applications on Big Data and Cognitive technologies including API development. Expected to have traditional Application Development background along with knowledge of Analytics libraries, open-source Natural Language Processing, statistical and big data computing libraries. Strong technical abilities to understand, design, write and debug complex code. Skills include Python, Spark, Kafka, GO, Hadoop, NoSQL, HBase, HIVE, PIG, C++, SQL, Linux, Java, EAI, SOA, CEP, HDFS, ETL

• Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
• Minimum of 3 years of experience in data engineering, with a focus on implementing data platforms, AI, and ML solutions.
• Hands-on experience with at least one major cloud provider: Azure, AWS, or GCP.
• Proficiency in programming languages such as Python, Py-Spark, PL/SQL, Spark SQL, GO, Java
• Experience with data processing frameworks like Apache Spark, Hadoop, or similar.
• Strong knowledge of SQL and experience with relational and non-relational databases.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes) and CI/CD pipelines.
• Solid understanding of data warehousing concepts and tools (e.g., DataBricks, Redshift, BigQuery).
• Knowledge of AI/ML concepts and experience in supporting AI/ML teams.
• Strong problem-solving skills, with the ability to troubleshoot complex data issues.
• Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders.

• Design, implement, and manage data platforms on cloud environments (Azure, AWS, or GCP) to support AI and ML workloads.
• Develop, test, and maintain robust data pipelines that ensure the smooth ingestion, processing, and storage of large datasets from diverse sources.
• Integrate data solutions with cloud-native services, ensuring optimal performance, scalability, and cost-effectiveness.
• Work closely with data scientists, data engineer, business analyst and AI/ML engineers to deploy models and algorithms into production, optimizing them for performance and scalability.
• Design and develop Extract, Transform, Load (ETL) processes to clean, enrich, and prepare data for analysis and machine learning tasks.
• Implement and manage data governance practices, ensuring data quality, security, and compliance with industry standards and regulations.
• Monitor and optimize the performance of data platforms and pipelines, identifying and resolving bottlenecks and inefficiencies.
• Create detailed documentation for data platforms and pipelines, and provide regular status updates to stakeholders.
• Stay updated with the latest trends and technologies in data engineering, AI, and cloud computing, and advocate for best practices within the team.