Jobs / IT, Software, Analytics / Job #1784

Senior Cloud Data Engineer – AWS Data Platforms

JobPartNet
India, India Contract Rs.4,00,00,000 - Rs.7,00,00,000 / hour

Job Description

Posted September 24, 2026
We are recruiting "Senior Cloud Data Engineer – AWS Data Platforms" for our client in India.

Senior Cloud Data Engineer – AWS Data Platforms
Job Details
Job Title: Senior Cloud Data Engineer / AWS Data Engineer
Experience: 7–9 Years
Location: Remote
Employment Type: Contract / C2C
Seniority: Senior
Primary Cloud: AWS
Availability: Immediate / Short Notice Preferred
Role Overview
We are looking for a Senior Cloud Data Engineer with strong hands-on experience in AWS data platforms, data engineering, ETL/ELT pipelines, data modelling, SQL, big data, cloud infrastructure, and CI/CD.
The candidate will be responsible for designing, developing, and operating scalable data solutions that integrate structured and unstructured data from internal, on-premises, cloud, and external sources.
The role requires strong expertise in AWS Glue, Amazon EMR, Apache Spark, Amazon Kinesis, Python, SQL, NoSQL, Terraform, Jenkins, and CI/CD, along with a solid understanding of data architecture, database technologies, security, and cloud operations.
Key Responsibilities
Data Engineering & Architecture
Design, develop, and implement scalable, high-performance cloud data solutions.
Build and maintain robust ETL/ELT pipelines for data ingestion, transformation, processing, and integration.
Design scalable data models based on business and technical requirements.
Integrate structured and unstructured data from:
On-premises databases
AWS/cloud platforms
External data sources
APIs and web services
Optimize data pipelines for performance, reliability, scalability, and cost efficiency.
Support Data Scientists, Researchers, Analytics, and Reporting teams with reliable data access and processing capabilities.
AWS Data Platform
Design, develop, and support AWS-based data platforms and data lakes.
Hands-on experience with:
AWS Glue
Amazon EMR
Amazon Kinesis
Amazon ECS
Configure, monitor, troubleshoot, and maintain AWS data-processing resources and clusters.
Monitor resource utilization and implement performance and cost optimizations.
Support provisioning, configuration, upgrades, migrations, and operational maintenance of AWS resources.
SQL & Database Engineering
Write, review, and optimize complex SQL queries.
Perform SQL query optimization and database performance tuning.
Design and manage scalable data models across different database technologies.
Work with:
Relational databases
NoSQL databases
Document databases
Key-value databases
Graph databases
Time-series databases
Support database administration activities including backup/recovery, upgrades, migrations, performance troubleshooting, availability, reliability, and cluster management.
Big Data & Streaming
Develop distributed data-processing solutions using Apache Spark.
Build and support real-time/streaming data pipelines using Amazon Kinesis and/or Apache Kafka.
Handle large-scale data processing, transformation, and ingestion workloads.
Ensure data pipelines are reliable, scalable, and fault tolerant.
Programming & APIs
Strong programming experience with Python.
Experience with Java and/or Scala is an advantage.
Develop and consume REST APIs and web services.
Integrate data platforms with internal and external applications.
Support data-harvesting and data-ingestion mechanisms where required.
DevOps, IaC & CI/CD
Automate infrastructure provisioning and configuration using Terraform / Infrastructure as Code (IaC).
Build and maintain CI/CD pipelines for data applications and platform deployments.
Hands-on experience with Jenkins or equivalent CI/CD tools.
Use Git for source-code and configuration management.
Automate deployment, configuration, and operational processes.
Continuously improve development, testing, and deployment workflows.
Machine Learning & Analytics Enablement
Support the deployment and operationalization of machine-learning models.
Enable Data Science and Analytics teams with reliable data pipelines and data-access solutions.
Support use cases involving:
Recommendation engines
Personalization
Search
Predictive analytics
Understand requirements for productionizing ML and data-science workloads.
Security & Data Governance
Implement data governance and access-control practices across cloud data platforms.
Follow AWS and cloud-security best practices.
Support security-risk reduction, data protection, and appropriate access management.
Ensure data solutions meet organizational security and compliance requirements.
Collaboration & Documentation
Work closely with Business Teams, Data Scientists, Data Analysts, Architects, and Engineering teams.
Translate business and technical requirements into practical data solutions.
Participate in solution design, technical troubleshooting, and implementation activities.
Prepare and maintain:
Technical documentation
Architecture/design specifications
Data-flow documentation
Operational procedures
Provide technical support and knowledge transfer to platform users and engineering teams.
Mandatory Technical Skills
AWS
AWS
AWS Glue
Amazon EMR
Amazon Kinesis
Amazon ECS
Data Engineering
ETL/ELT
Data Pipelines
Data Integration
Data Transformation
Data Processing
Data Lakes / Data Platforms
Scalable Data Architecture
Big Data & Streaming
Apache Spark
Kafka and/or Kinesis
Distributed Data Processing
Streaming/Event Processing
Programming
Python
Java/Scala – Advantage
Database & SQL
Advanced SQL
SQL Query Optimization
Database Performance Tuning
Relational Databases
NoSQL Databases
Understanding of Document, Key-Value, Graph, and Time-Series Databases
DevOps & Infrastructure
Terraform
Infrastructure as Code (IaC)
Jenkins
CI/CD
Git
Configuration and Deployment Automation
Integration
REST APIs
Web Services
On-premises and External-System Integration
Good to Have
Experience with Amazon ECS and containerized workloads.
Experience with both Apache Kafka and Amazon Kinesis.
Java or Scala development experience.
Experience with graph, document, key-value, or time-series databases.
Experience supporting ML model deployment.
Knowledge of recommendation and personalization systems.
Experience with TDD/BDD practices.
Knowledge of configuration-management tools.
Experience estimating infrastructure and resource requirements.
Experience with large-scale enterprise data platforms.
Required Experience
7–9 years of experience in Data Engineering / Cloud Data Engineering.
Strong experience designing and developing cloud-based data models and ETL pipelines.
Strong hands-on AWS experience.
Proven experience with AWS Glue, EMR, Spark, and Kinesis.
Strong SQL and database-engineering capabilities.
Experience with Infrastructure as Code and CI/CD.
Ability to troubleshoot performance, availability, and operational issues in data platforms.
Strong analytical, problem-solving, documentation, and stakeholder-management skills.
Key Skills
AWS | AWS Glue | Amazon EMR | Amazon Kinesis | Amazon ECS | Apache Spark | Python | SQL | NoSQL | ETL | Data Pipelines | Data Modelling | Data Lake | REST API | Terraform | IaC | Jenkins | CI/CD | Git | Kafka | Database Administration | Cloud Security | Data Governance
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