Senior Data Engineer

Who We Are

At Kyndryl, we design, build, manage and modernize the mission-critical technology systems that the world depends on every day. So why work at Kyndryl? We are always moving forward - always pushing ourselves to go further in our efforts to build a more equitable, inclusive world for our employees, our customers and our communities.

The Role

We're looking for exceptional talent to join our AI Agentic Innovation Hub at Kyndryl!

The AI Agentic Innovation Hub stands as Kyndryl's center of excellence for advanced and agentic artificial intelligence. Our mission is to lead the design and deployment of transformative AI solutions that bridge frontier research with real-world impact - scalable, secure, and driven by measurable value.
Built upon a team of exceptional talent and cutting-edge technology, the Hub embodies a spirit of bold innovation and disciplined execution - an elite unit within one of the world's leading technology companies. With national reach and global ambition, we partner with major organizations to tackle their most complex challenges, pioneering the next generation of intelligent, autonomous, and trusted systems that redefine what AI can achieve.

Job Description

As a Senior Data Engineer at Kyndryl's AI Innovation Hub, you will architect, build, and optimize large-scale data platforms that power advanced analytics and AI-driven solutions. You'll be responsible for designing robust data pipelines, ensuring data quality, and enabling seamless integration across cloud and on-premises environments. Working closely with data scientists, architects, and business stakeholders, you will transform complex data requirements into scalable, governed, and actionable data assets that drive enterprise innovation.

• Design, implement, and optimize ETL/ELT pipelines and data models for structured and unstructured data at scale.

• Build and maintain scalable data architectures supporting batch and real-time processing across cloud (AWS, Azure, GCP) and hybrid environments.

• Ensure data quality, lineage, and governance through robust validation frameworks and policies.

• Develop and maintain APIs and integrations for data access, supporting AI and analytics use cases.

• Collaborate with cross-functional teams to deliver data solutions aligned with business and AI needs.

• Mentor junior engineers and champion best practices in DataOps, CI/CD, and pipeline orchestration.

• Evaluate and integrate emerging data technologies, tools, and frameworks to enhance platform capabilities.

• 4+ years of experience building and maintaining large-scale data warehouses and data pipelines in enterprise environments.

• Strong programming skills in Python and SQL, with hands-on experience in ETL/ELT tools (e.g., Airflow, dbt, Databricks, Kafka).

• Expertise in data modeling, distributed systems, and cloud-native data services (AWS, Azure, GCP).

• Experience with BI tools such as Looker or PowerBI for data visualization and reporting.

• Knowledge of data governance, lineage, and observability tools.

• Experience with data pipeline tooling (Databricks, Cloudera, Teradata, Snowflake).

• Familiarity with DevOps/DataOps practices and CI/CD pipelines for data workflows.

• Fluent or native in Spanish; strong communication skills.

• Bachelor's or Master's degree in Computer Science, Mathematics, Engineering, or a related technical field.

• Postgraduate studies in Data Engineering, Cloud Computing, or Applied Data Science are valued.

• Professional certifications in cloud platforms (AWS, Azure, GCP) or data engineering tools are a plus.

• Demonstrated commitment to continuous learning and staying current with advances in data engineering and cloud technologies.

• Experience in regulated industries or environments with stringent compliance requirements.

• Knowledge of containerization and orchestration (Docker, Kubernetes).

• Familiarity with semantic modeling, metadata management, and emerging AI/ML data integration patterns.

• Cloud platform certification.

• Experience mentoring and leading technical teams.

• Analytical and structured thinking, with a focus on solving complex data challenges.

• Clear and adaptive communication, able to explain technical concepts to both technical and business audiences.

• Collaborative spirit, thriving in multidisciplinary teams.

• Proactive and innovative mindset, continuously exploring new tools and methodologies.

• Commitment to quality, reliability, and continuous improvement.