IBM Technical team lead - Data Engineer Integration 

Argentina, Autonomous City of Buenos Aires, Buenos Aires 

408279429

In this role, you’ll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers), where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world.​ Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.

Your Role and Responsibilities

As a Data Engineer at IBM you will harness the power of data to unveil captivating stories and intricate patterns. You’ll contribute to data gathering, storage, and both batch and real-time processing.

Collaborating closely with diverse teams, you’ll play an important role in deciding the most suitable data management systems and identifying the crucial data required for insightful analysis. As a Data Engineer, you’ll tackle obstacles related to database integration and untangle complex, unstructured data sets.

In this role, your responsibilities may include:

• Implementing and validating predictive models as well as creating and maintain statistical models with a focus on big data, incorporating a variety of statistical and machine learning techniques
• Designing and implementing various enterprise seach applications such as Elasticsearch and Splunk for client requirements
• Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviors.
• Build teams or writing programs to cleanse and integrate data in an efficient and reusable manner, developing predictive or prescriptive models, and evaluating modeling results

• Data Access Management: Ensure secure and efficient access to data across various platforms, including NoSQL and SQL databases, by implementing role-based access controls (RBAC), encryption, and other security measures.
• Compliance: Stay abreast of regulatory requirements such as GDPR, PCI-DSS, and ensure that all data practices meet these standards, including data masking, encryption, and maintaining audit trails.
• Security: Protect sensitive financial data from breaches and unauthorized access through robust cybersecurity measures, including intrusion detection systems, firewalls, and secure data transmission protocols.
• Cloud Computing: Leverage cloud technologies to enhance the scalability, availability, and cost-efficiency of data systems, including optimizing cloud resource usage and implementing disaster recovery plans.
• Data Governance: Establish and maintain data governance frameworks to ensure data quality, consistency, and accountability across the organization, including data cataloging, metadata management, and data stewardship programs.
• Optimization and Performance: Continuously monitor and optimize database performance to handle high volumes of transactions efficiently, involving database tuning, indexing, and query optimization.
• Strategic Alignment: Align data strategy with organizational goals to drive operational excellence, collaborating with other departments to understand their data needs and ensuring that data architecture supports the overall business strategy.
• Innovation and Best Practices: Stay current with industry trends and incorporate best practices in data architecture by adopting new technologies and methodologies to improve data management and security.

• Knowledge in AWS
• Data Pipelines
• Extract, Transform and Load (ETL)
• Version Control
• Solution Architecture