AWS Data Engineer
Summary
Data engineer at TCS designing and optimizing scalable ETL/ELT pipelines, data lakes, and data warehouse solutions on AWS. Core stack is Python, PySpark, and AWS data services such as S3, EMR, Glue, Athena, and Redshift; open to candidates with 6-12 years of experience across multiple Indian cities.
🚀 Walk-in Drive for AWS Data Engineer | Exciting Career Opportunities! 🚀
⚡ 3 cities | 1 day
Walk-in Interview Drive Date: 22-Aug-26 (Saturday)
⏰Registration Time: 9:00 AM to 12:30 PM
Experience: 5 to 15 years
Locations:
#Chennai Tata Consultancy Services : Siruseri Campus is located at Plot No. 1/G1, SIPCOT IT Park, Navalur Post, Siruseri, Chennai, Tamil Nadu 603103
#Bangalore Tata Consultancy Services: Think Campus 42, 45-P, Hosur Rd, Phase II, Konappana Agrahara, Karnataka 560100
#Pune Tata Consultancy Services: Sahyadri Park 2, Interview Bay, Plot No. 2 & 3, Phase 3, Rajiv Gandhi Infotech Park, Hinjewadi, Pune, Maharashtra, 411057
Job Title: AWS Data Engineer (Python & PySpark)
Experience:
6 – 12 Years
Location:
Pune / Bangalore / Hyderabad / Chennai / Mumbai
Job Description:
We are looking for an experienced AWS Data Engineer with strong expertise in Python, PySpark, and AWS Data Services to design, develop, and optimize scalable data pipelines and cloud-based data engineering solutions. The ideal candidate should have hands-on experience in developing large-scale data processing applications, data lake implementations, and ETL/ELT frameworks on AWS. (TCS_JD_Tem...Developer | Word), (TCS_JD_Tem...pr aws TRP | Word)
Must-Have Skills:
- Strong programming experience in Python
- Hands-on expertise in PySpark
- Experience with AWS services such as:
- S3
- EMR
- Glue
- Lambda
- IAM
- Athena
- Redshift
- Strong SQL and Data Warehousing concepts
- Experience in building ETL/ELT pipelines
- Data Lake/Data Warehouse implementation experience
- Git/GitHub version control
- Performance tuning and optimization of Spark applications
- Understanding of Agile development methodologies
Good-to-Have Skills:
- Databricks
- Apache Airflow
- dbt
- Snowflake
- Terraform
- Kafka
- CI/CD Pipelines
- Docker/Kubernetes
Roles & Responsibilities:
- Design, develop, and maintain scalable data pipelines using Python and PySpark.
- Build and optimize ETL/ELT processes on AWS cloud platforms.
- Develop batch and real-time data processing solutions.
- Work with large-scale structured and unstructured datasets.
- Create and maintain Data Lakes and Data Warehouse solutions.
- Perform data quality validation, monitoring, and troubleshooting.
- Collaborate with business stakeholders, architects, and development teams to gather requirements and deliver data solutions.
- Optimize Spark jobs for performance, scalability, and cost efficiency.
- Participate in code reviews and ensure adherence to coding standards.
- Support production deployments and resolve critical incidents.
Relevant Experience:
- 6–12 years of overall IT experience.
- Minimum 4+ years of hands-on experience in Python and PySpark development.
- Experience in AWS-based data engineering projects.
- Strong exposure to Data Warehousing and Data Lake architectures. (TCS_JD_Tem...Databricks | Word), (TCS_JD_Tem...Developer | Word)
Education:
- BE / B.Tech / MCA / M.Tech or equivalent.