Senior AWS Engineer

Fractal Analytics Inc.; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Fractal Analytics Inc. in Bengaluru is seeking a Senior AWS Engineer to design and optimize data ingestion, transformation, and analytics pipelines on AWS.

You will build reliable, scalable data flows and collaborate with data architects, DevOps, and business stakeholders to deliver trusted, query-ready data. The role emphasizes hands-on experience with AWS data services, strong data modeling, and distributed processing patterns to support reporting and NLP-driven insights across the ADC

Qualifications

• Hands-on experience with AWS data engineering services.
• Strong data modeling expertise for analytics.
• Solid understanding of distributed data processing patterns.

Responsibilities

• Develop, configure, and support data ingestion workflows using AWS Glue, Lambda, S3, EventBridge, and Redshift Serverless.
• Execute ingestion testing activities and validate data movement across raw, curated, and consumption layers.
• Monitor and support schema discovery, schema inference, and schema drift resolution processes.
• Troubleshoot and resolve data pipeline issues, event sequencing failures, and ingestion bottlenecks.
• Collaborate with senior engineers to maintain stable and reliable data flows across Development and UAT environments.
• Implement and maintain data models within Amazon Redshift to support reporting, analytics, and NLP-driven query capabilities.
• Optimize Redshift COPY operations, data loading strategies, partitioning, and performance tuning processes.
• Support normalization, transformation, and enrichment activities to improve data consistency and usability.
• Ensure datasets are query-ready for downstream business intelligence and AI-driven consumption.
• Maintain metadata integrity through AWS Glue Data Catalog and support governance processes using AWS Lake Formation.
• Implement and enhance data quality validation checks and reconciliation mechanisms.
• Ensure adherence to data governance, security, and access control standards.
• Monitor and improve data lineage, catalog accuracy, and data discoverability.
• Support implementation and configuration of Amazon QuickSight to enable self-service analytics and NLP-based querying capabilities.
• Collaborate with Platform and DevOps teams on QuickSight setup, dataset modeling, security integration, and NLP feature enablement.
• Ensure analytics datasets are optimized for performance, accuracy, and business consumption.
• Analyze application and pipeline behavior using Amazon CloudWatch Logs and monitoring tools.
• Support incident analysis, root cause investigations, and performance improvement initiatives.

Skills

AWS Glue

Amazon Redshift

Data modeling

ETL pipelines

AWS Lambda

S3

EventBridge

Data governance

QuickSight

CloudWatch

NLP enablement

Tools

Glue Crawlers

Redshift Serverless

AWS Lake Formation

Glue Data Catalog

AWS QuickSight

EventBridge

Job description

## Senior AWS EngineerApply: Bengaluru: Full time: Posted Today: SR-47598It's fun to work in a company where people truly BELIEVE in what they are doing!*We're committed to bringing passion and customer focus to the business.*Role OverviewWe are seeking a highly motivated **Senior Data Engineer (AWS)** to support the design, implementation, testing, and optimization of data ingestion, transformation, and analytics capabilities across the ADC platform. The role will focus on building reliable, scalable, and high-performance data pipelines leveraging AWS-native data services while ensuring data quality, governance, and operational excellence.The ideal candidate will possess hands-on experience with AWS data engineering services, strong data modeling expertise, and a solid understanding of distributed data processing patterns. This role will work closely with data architects, senior engineers, DevOps teams, and business stakeholders to enable trusted, query-ready data for reporting, analytics, and NLP-driven insights.### Key ResponsibilitiesData Ingestion & Pipeline Engineering* Develop, configure, and support data ingestion workflows using **AWS Glue, Glue Crawlers, AWS Lambda, Amazon S3, Amazon EventBridge, and Amazon Redshift Serverless**.* Execute ingestion testing activities and validate data movement across raw, curated, and consumption layers.* Monitor and support schema discovery, schema inference, and schema drift resolution processes.* Troubleshoot and resolve data pipeline issues, event sequencing failures, and ingestion bottlenecks.* Collaborate with senior engineers to maintain stable and reliable data flows across Development and UAT environments.Data Warehousing & Modeling* Implement and maintain data models within **Amazon Redshift** to support reporting, analytics, and NLP-driven query capabilities.* Optimize **Redshift COPY operations**, data loading strategies, partitioning, and performance tuning processes.* Support normalization, transformation, and enrichment activities to improve data consistency and usability.* Ensure datasets are query-ready for downstream business intelligence and AI-driven consumption.Data Governance & Quality* Maintain metadata integrity through **AWS Glue Data Catalog** and support governance processes using **AWS Lake Formation**.* Implement and enhance data quality validation checks and reconciliation mechanisms.* Ensure adherence to data governance, security, and access control standards.* Monitor and improve data lineage, catalog accuracy, and data discoverability.Analytics & NLP Enablement* Support implementation and configuration of **Amazon QuickSight** to enable self-service analytics and NLP-based querying capabilities.* Collaborate with Platform and DevOps teams on QuickSight setup, dataset modeling, security integration, and NLP feature enablement.* Ensure analytics datasets are optimized for performance, accuracy, and business consumption.Monitoring & Operational Excellence* Analyze application and pipeline behavior using **Amazon CloudWatch Logs** and monitoring tools.* Support incident analysis, root cause investigations, and performance improvement initiatives.* Contribute to operational documentation, runbooks, deployment procedures, and support guides.* Promote reproducibility, automation, and engineering best practices across the data platform.Mandatory:AWS