Data Engineer Python

Advance Career Solutions; India; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Advance Career Solutions is seeking a Data Engineer with strong Python, PySpark, AWS Glue, Snowflake, AWS services, and SQL expertise. You will design and optimize scalable data pipelines and cloud-based data solutions for PAN India operations.

The role requires hands-on development of ETL/ELT workflows, data warehousing with Snowflake, and cloud-native data tooling. Strong problem-solving and collaboration are essential for delivering robust data solutions.

Qualifications

• 3–8 years of experience in Data Engineering.
• Hands-on experience with Python, PySpark, AWS Glue, Snowflake, AWS services, and SQL.
• Ability to design scalable data pipelines and cloud-based data solutions.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Python and PySpark.
• Build and manage ETL/ELT workflows using AWS Glue.
• Work with Snowflake for data warehousing and processing.
• Develop and optimize data solutions using AWS S3, Lambda, and CloudWatch.
• Write efficient SQL queries for data extraction, transformation, and analysis.
• Perform data pipeline and application performance tuning.
• Implement data integration across multiple data sources and platforms.
• Ensure data quality, reliability, scalability and availability of data pipelines.
• Troubleshoot and resolve data pipeline and production issues.
• Collaborate with data engineers, architects, analysts, and other stakeholders to deliver robust data solutions.

Skills

Python

PySpark

SQL

ETL/ELT

Data Pipelines

Tools

AWS Glue

Snowflake

AWS S3

AWS Lambda

AWS CloudWatch

Job description

Data Engineer Python + PySpark

Skill: Data Engineer – Python + PySpark
Job Requisition No.: 1487899
Experience: 3–8 Years
Location: PAN India
Role: Data Engineer – PySpark + AWS Glue

Job Description

We are looking for an experienced Data Engineer with strong hands‑on expertise in Python, PySpark, AWS Glue, Snowflake, AWS services, and SQL. The candidate will be responsible for developing and optimizing scalable data pipelines, ETL/ELT workflows, and cloud‑based data solutions.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Python and PySpark.
• Build and manage ETL/ELT workflows using AWS Glue.
• Work with Snowflake for data warehousing and data processing.
• Develop and optimize data solutions using AWS S3, Lambda, and CloudWatch.
• Write efficient and optimized SQL queries for data extraction, transformation, and analysis.
• Perform data pipeline and application performance tuning.
• Implement data integration solutions across multiple data sources and platforms.
• Ensure data quality, reliability, scalability, and availability of data pipelines.
• Troubleshoot and resolve data pipeline and production issues.
• Collaborate with data engineers, architects, analysts, and other stakeholders to deliver robust data solutions.

Required Skills

• 3–8 years of experience in Data Engineering.
• Strong hands‑on experience with:
  
  • Python
  • PySpark
  • AWS Glue
  • Snowflake
  • AWS S3
  • AWS Lambda
  • AWS CloudWatch
  • SQL
• Strong understanding of:
  
  • ETL / ELT
  • Data Warehousing
  • Data Pipelines
  • Data Integration
  • Performance Tuning
  • Cloud Data Platforms

Good to Have

• Exposure to AI/ML or GenAI solutions.
• Knowledge of Oracle Fusion Cloud / Oracle Applications.
• Experience working with large‑scale data processing and cloud‑native data engineering solutions.

Ideal Candidate

The ideal candidate should have strong practical experience in Python + PySpark + AWS Glue, along with good knowledge of Snowflake and AWS cloud services. Strong problem‑solving, debugging, and communication skills are expected.

Immediate/short notice candidates are preferred.