Data Engineer – PySpark, Python, AWS (US Work Support)
Budget: ₹37,500 – ₹75,000 INR
Job Title: Data Engineer – PySpark, Python, AWS (US Work Support)
Experience: 10+ Years
Budget: ₹35,000 – ₹40,000 per month
Type: US Work Support
Job Description:
We are looking for a Data Engineer to provide US work support, with strong hands-on experience in PySpark, Python, and AWS services. The role involves developing, optimizing, and maintaining large-scale data processing pipelines and cloud-based solutions.
Key Responsibilities:
Design and build ETL/ELT pipelines using PySpark and Python.
Ingest, transform, and load data into data lakes and data warehouses.
Optimize distributed data jobs using Apache Spark (PySpark).
Work extensively with AWS services — S3, EC2, Lambda, Glue, Redshift, Athena, EMR, Kinesis, Step Functions.
Support integration of machine learning data pipelines in production.
Troubleshoot and enhance performance of existing data workflows.
Required Skills:
Python: Strong programming and scripting skills.
PySpark / Apache Spark: Advanced experience in distributed data processing.
AWS Services: Expertise with S3, Glue, Lambda, EMR, Redshift, Athena, Step Functions.
Databases: Proficient in SQL with PostgreSQL/MySQL.
Knowledge of streaming tools (Kinesis/Kafka) is an added advantage.
Solid understanding of data modeling and data governance.
Experience: 10+ Years
Budget: ₹35,000 – ₹40,000 per month
Type: US Work Support
Job Description:
We are looking for a Data Engineer to provide US work support, with strong hands-on experience in PySpark, Python, and AWS services. The role involves developing, optimizing, and maintaining large-scale data processing pipelines and cloud-based solutions.
Key Responsibilities:
Design and build ETL/ELT pipelines using PySpark and Python.
Ingest, transform, and load data into data lakes and data warehouses.
Optimize distributed data jobs using Apache Spark (PySpark).
Work extensively with AWS services — S3, EC2, Lambda, Glue, Redshift, Athena, EMR, Kinesis, Step Functions.
Support integration of machine learning data pipelines in production.
Troubleshoot and enhance performance of existing data workflows.
Required Skills:
Python: Strong programming and scripting skills.
PySpark / Apache Spark: Advanced experience in distributed data processing.
AWS Services: Expertise with S3, Glue, Lambda, EMR, Redshift, Athena, Step Functions.
Databases: Proficient in SQL with PostgreSQL/MySQL.
Knowledge of streaming tools (Kinesis/Kafka) is an added advantage.
Solid understanding of data modeling and data governance.