Company Overview :

Infosys is a global leader in next-generation digital services and consulting. With a presence in over 50 countries, the company enables clients to navigate their digital transformation by leveraging cloud, AI, and data analytics. Infosys operates across diverse industries including financial services, retail, manufacturing, and healthcare, managing complex enterprise ecosystems at a massive scale to drive operational efficiency and innovation for Fortune 500 organizations.

Role Overview :

As a Principal Data Engineer, you will serve as a technical authority in designing and implementing robust data ecosystems that power enterprise-level decision-making. You will work closely with cross-functional teams, including data scientists, architects, and business stakeholders, to translate complex business requirements into scalable data solutions. Your work will directly influence the reliability and performance of data-driven products, ensuring that the organization can derive actionable insights from high-velocity data streams in a secure and efficient manner.

Key Responsibilities :

- Architect and oversee the development of end-to-end data pipelines to ensure seamless data ingestion, transformation, and delivery for large-scale analytics platforms.

- Lead the design of high-performance data architectures that support both batch and real-time processing requirements for global clients.

- Optimize existing ETL frameworks to improve data quality, reduce latency, and enhance the overall reliability of the data ecosystem.

- Implement streaming solutions using Kafka to facilitate real-time data integration across distributed enterprise systems.

- Mentor junior engineering teams by establishing best practices in code quality, system design, and performance tuning to foster a culture of technical excellence.

- Collaborate with stakeholders to identify data bottlenecks and deploy innovative engineering solutions that align with long-term business objectives.

Required Skillset :

- Demonstrated expertise in designing complex data architectures and managing large-scale Big Data environments.

- Proficiency in Python for building scalable data processing applications and automating complex workflows.

- Advanced knowledge of ETL methodologies and the ability to design resilient pipelines that handle high-volume data sets.

- Hands-on experience with Kafka for building event-driven architectures and real-time data streaming solutions.

- Strong analytical and problem-solving abilities, with the capacity to communicate technical concepts clearly to non-technical stakeholders.

- Ability to thrive in a collaborative, hybrid work environment based in Bangalore, maintaining high standards of delivery while working with global teams.

- A degree in Computer Science, Engineering, or a related quantitative field, supported by a track record of delivering high-impact data engineering projects.