Description

Cloud & Data Engineering (AWS)

• Strong hands-on experience with:
  • Amazon S3
  • AWS Glue
  • Amazon Athena
  • Amazon Redshift
  • Amazon EMR
• Experience designing cloud-native data lakes and data warehouse architectures
• Solid understanding of batch data processing and basic exposure to streaming concepts

SQL & Python (Mandatory)

Strong SQL skills (mandatory):

• Complex queries, joins, aggregations, and transformations
• Experience working with large datasets in Redshift/Athena

Strong Python skills (mandatory):

• Python for data engineering and ETL use cases
• Experience with PySpark / Spark (preferred)

Good understanding of:

• Data modeling
• Transformations
• Performance tuning

Data Processing & Engineering

• Hands-on experience with Spark / PySpark
• Experience handling:
  • Structured and semi-structured data
• Knowledge of:
  • Schema evolution
  • Data quality checks
  • Validation logic

DevOps & Platform Basics

• Working knowledge of Infrastructure as Code (Terraform / CloudFormation)
• Basic experience with CI/CD pipelines for data workloads
• Understanding of logging and monitoring using AWS CloudWatch

Collaboration

• Ability to work with architects, DevOps, QA, and business stakeholders
• Good communication skills to clearly explain technical concepts

4. Good-to-Have Skills

• Experience with streaming technologies (Amazon Kinesis / Kafka)
• Familiarity with Lakehouse and modern data platform architectures
• Integration experience with BI / reporting tools
• Basic knowledge of:
  • Data governance
  • Data quality
  • Metadata management
• Awareness of AWS cost optimization (FinOps basics)
• Experience in Agile delivery models with global teams
• Exposure to AI / ML use cases

5. Key Responsibilities

Data Engineering & Development

• Design and build scalable ETL/ELT pipelines on AWS
• Develop:
  • SQL-based data transformations
  • Python-based data pipelines
• Implement data ingestion pipelines using S3, Glue, EMR
• Build data models optimized for analytics, performance, and cost efficiency

Platform & Operations

• Support deployment and execution of data pipelines
• Monitor:
  • Pipeline performance
  • Reliability
  • Data quality
• Troubleshoot data issues and perform root cause analysis
• Apply best practices for:
  • Security
  • Reliability
  • Scalability

Collaboration & Delivery

• Work with architects and product teams to understand requirements
• Translate business needs into AWS data engineering solutions
• Contribute to:
  • Documentation
  • Code reviews
  • Engineering best practices

6. Education Qualification

• Bachelor’s or Master’s degree (or equivalent) in:
  • Computer Science
  • Information Technology
  • Data Engineering
  • or related field

7. Certifications (Preferred)

• AWS Certified:
  • Solutions Architect
  • DevOps (Professional)
• Snowflake Core Certification (optional)

Key Skills

Education

Bachelor's degree