In this role, you will act as the technical anchor for our data engineering initiatives, collaborating closely with cross-functional product teams, data scientists, and business stakeholders to transform raw data into actionable intelligence.

You will be responsible for architecting robust, scalable pipelines that drive critical business decisions and enhance customer experiences.

By leveraging the full breadth of the AWS ecosystem, you will ensure our data infrastructure remains performant, secure, and cost-effective while mentoring junior engineers to foster a culture of technical excellence across our Gurgaon, Bangalore, and Pune offices.

Key Responsibilities :

- Architect and deploy end-to-end data pipelines using AWS Glue and PySpark to ensure seamless data ingestion and transformation for high-volume analytical workloads.

- Optimize data storage and retrieval strategies within Amazon Redshift to improve query performance and support complex reporting requirements for business stakeholders.

- Design serverless and containerized data processing solutions using AWS Lambda and ECS to increase system reliability and reduce operational overhead.

- Manage and scale distributed computing clusters via Amazon EMR and EC2 to handle massive datasets with high availability and fault tolerance.

- Implement best practices for data governance, security, and monitoring to ensure data integrity and compliance across all cloud-based environments.

- Lead technical design reviews and code audits to maintain high engineering standards and promote the adoption of modern data architecture patterns within the team.

Required Skillset :

- Demonstrated expertise in building complex data platforms using Python and PySpark, with a deep understanding of distributed systems and data modeling.

- Advanced proficiency in the AWS ecosystem, specifically AWS Glue, Lambda, EMR, Redshift, EC2, and ECS, with a proven track record of managing production-grade cloud infrastructure.

- Strong analytical mindset with the ability to translate ambiguous business requirements into scalable technical specifications and high-performance data solutions.

- Exceptional communication and interpersonal skills, capable of articulating complex technical concepts to non-technical stakeholders and driving consensus within cross-functional teams.

- Proven ability to lead and mentor engineering talent, fostering a collaborative environment that encourages continuous learning and technical growth.

- A Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field, combined with the flexibility to thrive in a hybrid work environment.

- 7 to 12 years of professional experience in data engineering, with a focus on delivering high-impact data products in a fast-paced, cloud-native environment.