Data Engineer - AWS - GO/JC/4750/2026

Golden Opportunities; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Golden Opportunities is seeking a Data Engineer specialized in AWS to design and maintain robust data pipelines. You will build flows on AWS Glue, feed data into Amazon QuickSight and support dashboards with timely, quality data.

You will handle data ingestion, transformation, and dashboard refresh cycles, ensuring reliable BAU operations and adherence to SLAs. Exposure to Bedrock and SQL/Python will be valuable for integration and analytics tasks.

Qualifications

• 4+ years overall data engineering experience.
• 3+ years relevant AWS data engineering experience.

Responsibilities

• Build and maintain data pipelines on AWS Glue feeding QuickSight datasets with quality and timelines.
• Manage data ingestion, transformation and dashboard refresh schedules.
• Monitor pipeline health and resolve data-load and refresh issues within SLAs (BAU/L2-L3).
• Apply data-quality checks and reconciliations to protect metric accuracy.
• Deliver data behind new and enhanced dashboards following deployment best practices.
• Collaborate with architects, administrators and BI developers on delivery.
• Exposure to SQL and Python.
• Exposure to Amazon Bedrock.

Skills

Data pipelines

Education

Any Graduation

Tools

AWS Glue

Lake Formation

Athena

S3

Amazon Quick Sight

SQL

Python

Amazon Bedrock

Job description

Job title

Data Engineer - AWS

Location

Bangalore

Work Mode

Work From Office

Candidate Specification

4+ years overall, Relevant – 3+ years

Candidate Location

Pan India

Skills

AWS Glue, Lake Formation, Athena, S3, Amazon Quick Sight, SQL, Python, Amazon Bedrock.

Candidate Industry

IT

Education and Certification

Any Graduation

Job Description

Build and maintain data pipelines on AWS (Glue) that feed the Amazon Quick Suite (Quick Sight) datasets, to agreed quality and timelines.

Manage data ingestion, transformation and dashboard refresh schedules.

Monitor pipeline health and resolve data-load and refresh issues within agreed SLAs (BAU / L2-L3 support).

Apply data-quality checks and reconciliations to protect metric accuracy.

Deliver the data behind new and enhanced dashboards, following good deployment practice.

Collaborate with architects, administrators and BI developers on delivery.

AWS Glue, Lake Formation, Athena, S3, Amazon Quick Sight,

Exposure to SQL and Python;

Exposure to Amazon Bedrock.