AWS Data Engineer

Cloudesign; Bengaluru; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

A technology-driven data solutions company is seeking a Data Engineer in Bengaluru, India. The ideal candidate will have over 5 years of experience in data engineering, particularly with AWS services and strong skills in SQL and Python. Responsibilities include designing scalable data architectures, building ETL/ELT pipelines, and mentoring junior engineers. This role is critical in ensuring the effectiveness and reliability of data processes within the organization.

Qualifications

• 5+ years of professional experience in data engineering.
• 4+ years working with AWS services.
• Strong proficiency in SQL and Python for data processing.

Responsibilities

• Lead the design and implementation of scalable data architectures.
• Build and optimize ETL/ELT pipelines for diverse data sources.
• Drives data governance, security, and compliance initiatives.

Skills

Data engineering

AWS services

SQL

Python

Spark

CI/CD pipelines

Git

Docker

Kubernetes

Job description

Responsibilities

• Lead the design and implementation of scalable data architectures (data lakes, warehouses, streaming systems).
• Build and optimize ETL/ELT pipelines for diverse data sources, ensuring high performance and reliability.
• Drive data governance, security, and compliance initiatives across all data platforms.
• Mentor junior engineers and provide technical guidance to cross-functional teams.
• Collaborate with stakeholders to translate business requirements into technical solutions.
• Implement automation and monitoring frameworks to ensure operational excellence.
• Evaluate and adopt emerging AWS services and modern data engineering tools to enhance capabilities.

Mandatory Skills

• 5+ years of professional experience in data engineering, with at least 4+ years working on AWS.
• Deep expertise in AWS services: S3, Glue, Redshift, Athena, EMR, Kinesis, DynamoDB, Lambda, Step Functions.
• Strong proficiency in SQL, Python, and Spark for data processing and pipeline development.
• Proven experience with workflow orchestration tools (Airflow, Dagster, Step Functions).
• Solid understanding of data modeling, partitioning strategies, and performance tuning.
• Hands-on experience with CI/CD pipelines, Git, and Infrastructure-as-Code (Terraform/CloudFormation).
• Familiarity with containerization (Docker, Kubernetes) and microservices-based architectures.