Senior AWS Data Engineer

Fractal Analytics Private Limited; Bengaluru; On-site; INR 900,000 - 1,400,000; Full time

Job summary

Fractal Analytics Private Limited in Bengaluru is seeking a Senior AWS Engineer to design, build, and optimize scalable data pipelines using AWS data services for ingestion, transformation, and analytics. You will collaborate with data architects and DevOps teams to ensure data quality and governance across the data lake and warehouse landscape.

The role emphasizes hands-on experience with AWS data engineering services, strong data modeling skills, and the ability to enable NLP-ready datasets

Qualifications

• Hands-on experience designing and implementing AWS-based data pipelines.
• Experience with data modeling for analytics and NLP-ready datasets.
• Strong knowledge of data governance, security and lineage.

Responsibilities

• Develop, configure and optimize data ingestion workflows using AWS services.
• Build and maintain data models in Amazon Redshift Serverless for reporting and analytics.
• Ensure data quality, governance and lineage across raw, curated and consumption layers.
• Configure and optimize NLP-enabled analytics dashboards with QuickSight.

Skills

Data Ingestion

Data Pipelines

Data Modeling

ETL

Analytics

NLP

Cloud Data

Tools

AWS Glue

Glue Crawlers

AWS Lambda

Amazon S3

Amazon EventBridge

Amazon Redshift Serverless

Amazon QuickSight

AWS Lake Formation

CloudWatch

Job description

Senior AWS Engineer

It'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 Overview

We 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 Responsibilities

• Data 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

Experience Level Senior Level