Data Engineer - AWS

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

Job summary

Golden Opportunities seeks an experienced data engineer to build and maintain scalable AWS data pipelines (Glue) feeding QuickSight datasets. You will manage ingestion, transformation, and dashboard refresh schedules, ensuring data quality and timely delivery.

The role requires 4+ years in data engineering, with 3+ years in relevant work, and comfortable working from office across India. Exposure to Bedrock and SQL/Python will be advantageous.

Qualifications

• 4+ years overall, relevant 3+ years experience in data engineering.
• Work from Office; pan-India eligibility.
• Exposure to AWS data services and BI dashboards.

Responsibilities

• 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.
• Exposure to Amazon Bedrock.

Skills

SQL

Python

Education

Any Graduation.

Tools

AWS Glue

Lake Formation

Athena

S3

Amazon QuickSight

Amazon Bedrock

Job description

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.