Data Engineer

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 client 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. Visit us at .

We are looking for Sr. Data Engineer

Data Engineer

Experience: 5+ Years
Location: Trivandrum | Kochi | Chennai | Pune | Bangalore | Noida

Job Summary

We are looking for an experienced Data Engineer with 8+ years of experience in designing, building, and maintaining scalable data pipelines and data processing workflows on AWS . The ideal candidate should have strong hands-on expertise in AWS Glue, Python, Amazon S3, and large-scale data transformation and ingestion workflows .

Experience with AWS Bedrock will be an added advantage.

Key Responsibilities

• Design, build, and maintain scalable data pipelines on AWS .
• Develop and manage data ingestion and transformation workflows using AWS Glue .
• Perform large-scale data transformation and processing using Python and related data engineering technologies.
• Work with Amazon S3 for data storage and data lake solutions.
• Develop reliable and efficient ETL/ELT pipelines for large volumes of data.
• Ensure data pipelines are scalable, performant, reliable, and maintainable.
• Collaborate with cross-functional teams to understand data requirements and deliver effective data solutions.
• Monitor, troubleshoot, and optimize data pipelines and transformation workflows.
• Work with data visualization and analytics solutions such as Amazon QuickSight .
• Explore and integrate emerging AWS capabilities, including AWS Bedrock , where applicable.

Mandatory Skills

• AWS Glue
• Python
• AWS Data Pipelines
• Large-Scale Data Transformation
• Data Ingestion Workflows
• Amazon S3

Optional / Good-to-Have Skills

• PySpark
• Amazon QuickSight
• AWS Bedrock

Technical Skills

• AWS
• AWS Glue
• Amazon S3
• Python
• Data Pipelines
• ETL / ELT
• Large-Scale Data Transformation
• Data Ingestion
• PySpark
• Amazon QuickSight
• AWS Bedrock

Preferred Candidate Profile

• 5+ years of experience in Data Engineering .
• Strong hands-on experience building and maintaining AWS-based data pipelines .
• Strong expertise in AWS Glue and Python .
• Experience handling large-scale data transformation and ingestion .
• Good understanding of AWS data storage and processing services.
• Exposure to PySpark, QuickSight, and AWS Bedrock will be an advantage.

• What we believe :

  We re proud to embrace the same values that have shaped UST since the beginning. Since day one, we ve been building enduring relationships and a culture of integrity. And today, its those same values that are inspiring us to encourage innovation from everyone, to champion diversity and inclusion and to place people at the centre of everything we do.

  Humility:

  We will listen, learn, be empathetic and help selflessly in our interactions with everyone.

  Humanity:

  Through business, we will better the lives of those less fortunate than ourselves.

  Integrity:

  We honour our commitments and act with responsibility in all our relationships.

  Equal Employment Opportunity Statement

  UST is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation.

  All employment decisions shall be made without regard to age, race, creed, colour, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.

  
  UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.

  - To support and promote the values of UST.

  - Comply with all Company policies and procedures

AWS, AWS Glue, Amazon S3