Role Description
Who we are
At UST, we help the world’s best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our clients to co-create lasting change. Together, with over 30,000 employees in 30+ countries, we build for boundless impact, touching billions of lives in the process.
Summary
We are looking for an experienced Data Engineer with a strong background in building and maintaining scalable data pipelines and cloud infrastructure. The ideal candidate will have hands-on expertise in Python, PySpark, Terraform, and AWS, along with a solid understanding of data engineering best practices.
Senior Data Platform Engineer
Experience: 6+ Years Location: Hyderabad
Key Responsibilities
- Design, build, and maintain robust, scalable, and effective data pipelines for batch and streaming data processing. Develop and optimize ETL/ELT workflows using Python and PySpark to process large-scale datasets.
- Architect and manage cloud infrastructure on AWS (S3, EMR, Glue, Lambda, Redshift, EC2, IAM, etc.).
- Use Terraform to provision, manage, and version-control infrastructure as code (IaC). Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver reliable data solutions.
- Ensure data quality, integrity, and security across pipelines and storage systems.
- Monitor and troubleshoot production data pipelines, ensuring high availability and performance.
- Implement CI/CD pipelines for data engineering workflows.
- Document technical designs, data flows, and architecture for internal reference.
- Mentor junior data engineers and contribute to best practices within the team.
Required Skills & Qualifications
- 6+ years of experience in data engineering or a related field. Strong proficiency in Python for data processing and automation.
- Hands-on experience with PySpark for large-scale distributed data processing.
- Solid experience with Terraform for in
📌 Data platform Engineer (Hyderabad)
🏢 UST
📍 Hyderabad