Principal Engineer – GBS IND

Key Responsibilities

• Lead the architecture and engineering of modern, scalable cloud-native data platform solutions leveraging modern frameworks and technologies
• Design and implement robust data pipelines (batch and streaming) that support engineering metrics, software telemetry, and operational insights
• Drive the modernization of legacy data infrastructure, supporting both batch and real-time data processing
• Drive adoption of best practices in data engineering, including data modeling, data quality, data governance, and DevOps for data
• Collaborate with Product Owners, Users, data analysts and Software engineers to understand data needs and deliver robust data pipelines
• Evaluate ad introduce emerging technologies to improve the data platform’s agility, scalability, and performance
• Establish and enforce architectural guidelines, design patterns, and standards across data engineering initiatives
• Apply secure-by-design principles across data pipelines and storage, incorporating threat modeling and privacy-aware data handling. Ensure robust security, privacy, and compliance standards are embedded in data solutions
• Champion the responsible adoption of Generative AI tools for code generation, pipeline optimization, metadata management, and data quality enhancement
• Mentor and guide other engineers, play a key role in technical leadership and team development
• Partner with product and engineering leaders to align data initiatives with business outcomes

Qualification & Requirements

Education

• BE/MCA

Experience Range

• 15 – 20 Years

Foundational Skills

• Deep understanding of data modeling, ETL/ELT, data pipelines and data warehousing concepts
• Proven expertise in building ELT/ETL pipelines using tools like Apache Spark, Airflow, Kafka, and cloud-native orchestration frameworks
• Strong proficiency in SQL and programming languages such as Python or Java
• Strong knowledge of threat modeling and data security practices to proactively identify and mitigate risks in data pipelines and storage systems
• Practical use of Generative AI tools (e.g.: GitHub CoPilot) for accelerating development, testing, and pipeline refactoring
• Proven leadership, collaboration, and communication skills to drive cross-functional initiatives
• Ability to influence data strategy and architectural decisions while mentoring engineers, and foster a data-driven engineering culture

Desired Skills

• Experience modernizing data platforms using Lakehouse, data mesh, or event-driven architectural patterns
• Working knowledge of data governance, privacy frameworks, and secure cloud deployments
• Exposure to Kubernetes, Docker, and Observability practices in data engineering context
• Nice to have experience with cloud-native data platforms (e.g.: Databricks, Azure Data services)
• Exposure to AI/ML workflows, including integration with ML APIs, and orchestration of AI-powered features
• Ability to evaluate and adopt Generative AI capabilities (e.g.: LLM Integration, AI-assisted coding, architecture suggestion engines) to enhance developer and user experiences

Benefits

• Insurance Benefits
• Retirement Benefits
• Vacation Policy
• Other Perks and Benefits…