Job highlights

- 3+ years in Data Engineering with expertise in Python, PySpark, SQL, and AWS data services

- Develop and maintain batch and real-time data pipelines, build ETL workflows, monitor pipeline execution, and collaborate with teams to deliver data solutions

Key Responsibilities

- Develop and maintain batch and real-time data pipelines to support business requirements.

- Build and maintain ETL workflows for data ingestion, transformation, and integration.

- Work with data warehouses and data lakes to support data storage and processing requirements.

- Monitor pipeline execution, investigate data discrepancies, and resolve technical issues.

- Perform unit testing, data validation, and code debugging to ensure data accuracy and reliability.

- Collaborate with senior engineers, analysts, and business teams to deliver data engineering solutions.

- Follow established coding standards, documentation practices, and deployment procedures.

Mandatory Requirements





- 3+ years of relevant experience in Data Engineering.

- Solid hands-on proficiency in Python programming and OOP concepts.

- Good knowledge of SQL, including complex queries, joins, and query optimization.

- Practical experience with Apache Spark / PySpark for distributed data processing.

- Hands-on experience with AWS data services such as AWS Glue, Amazon S3, Lambda, Redshift, and Athena.

- Understanding of ETL/ELT processes, data warehouses, and data lakes.

- Familiarity with data quality, data validation, and data governance principles.

- Working knowledge of Git and Agile development practices.

Preferred

- Exposure to AWS EMR, Glue Data Catalog, and Lake Formation.

- Experience in BFSI, NBFC, or FinTech.

- Familiarity with JIRA, Confluence, Jenkins, or dashboard tools such as Tableau and Amazon QuickSight.

📌 Data Engineer (Mumbai)
🏢 Accord Infosystems
📍 Mumbai