Role Overview:

We are seeking a highly skilled Data Engineer to join our Bangalore-based engineering team to architect and maintain robust data pipelines that power our core analytical products. In this role, you will work closely with data scientists, product managers, and software architects to transform raw, disparate data into actionable insights that drive strategic business decisions. By designing scalable ETL frameworks and optimizing data workflows, you will directly influence the efficiency of our data infrastructure, ensuring that our stakeholders have reliable, high-quality data to support rapid business growth and innovation.

Key Responsibilities:

- Design and implement end-to-end ETL pipelines using Python and PySpark to ensure seamless data ingestion and transformation for downstream analytics.

- Optimize existing data architectures on AWS to improve processing speed and reduce operational costs for the business.

- Collaborate with cross-functional teams to translate complex business requirements into technical data solutions that enhance product performance.

- Maintain high standards of data integrity and security by implementing rigorous testing and monitoring protocols across all data environments.

- Mentor junior engineers and contribute to technical documentation to foster a culture of knowledge sharing and engineering excellence within the team.

Required Skillset:

- Demonstrated expertise in building complex data pipelines using Python and PySpark, with a deep understanding of distributed computing principles.

- Proven ability to architect and manage cloud-based data solutions within the AWS ecosystem, including services like S3, Redshift, and Glue.

- Strong analytical mindset with the ability to communicate technical complexities to non-technical stakeholders effectively.

- Exceptional problem-solving skills and the capacity to adapt to evolving project requirements in a fast-paced, hybrid work environment.

- A Bachelors or Masters degree in Computer Science, Engineering, or a related quantitative field, backed by 4 - 10 years of professional experience in data engineering.

- Ability to work collaboratively in a team-oriented setting, demonstrating strong interpersonal skills and a proactive approach to project ownership.