Technical Lead (AWS Data Engineering Lead)

Merkle Schweiz; Mumbai; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Merkle Schweiz is looking for a Technical Architect (AWS Data Engineering Lead) in Mumbai, India. The ideal candidate should have 6 to 10 years of hands-on experience designing and delivering high-performance data platforms on AWS.

This role demands proficiency in AWS data services, SQL, and Python, alongside strong communication skills for stakeholder collaboration. A Bachelor's or Master's degree in a related field is preferred.

Qualifications

• 6 to 10 years of experience in a hands-on role.
• Deep expertise in AWS data services, SQL, and Python.
• Experience with large-scale datasets in Redshift/Athena.

Responsibilities

• Design and implement AWS-based data engineering solutions.
• Build and optimize batch and streaming data pipelines.
• Ensure solutions meet security, reliability, and scalability standards.

Skills

AWS data services

SQL

Python

Spark

Data modeling

Terraform

Performance optimization

CI/CD pipelines

Education

Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or related field

Tools

AWS Glue

Redshift

Docker

Job description

About the job

Job Description – Technical Architect (AWS Data Engineering Lead)

1. Basic Information

• Job Title: Technical Architect (AWS Data Engineering Lead)
• Experience: 6 to 10 Years
• Shift Timing: 12 PM – 9 PM IST and/or 2 PM – 11 PM IST

2. Role Overview

We are looking for a hands-on AWS Technical Lead – Data Engineering with 6 to 10 years of experience to design and deliver scalable, secure, and high-performance data platforms on AWS.

This role requires strong individual contribution with end-to-end technical ownership. The candidate will collaborate closely with global teams, architects, and clients to deliver enterprise-grade data engineering solutions.

The ideal candidate should have deep expertise in AWS data services, SQL, and Python, along with the ability to design, build, and optimize reliable data pipelines for analytics and business use cases.

3. Must-Have Skills

• Strong hands-on experience with:
  • Amazon S3, AWS Glue, Athena, Redshift
• Experience designing cloud-native data lakes and data warehouses
• Deep understanding of batch and streaming data pipelines
• Experience building scalable and fault-tolerant data workflows

SQL & Python (Mandatory)

• Strong expertise in SQL, including:
  • Complex transformations and aggregations
  • Performance tuning and analytics queries
• Experience working with large-scale datasets in Redshift/Athena

PySpark / Spark Processing

• Hands‑on experience with Spark / PySpark
• Build reusable ETL components and utilities
• Strong understanding of:
  • Data modeling
  • Transformations
  • Performance optimization
• Experience with distributed processing frameworks (Spark/PySpark)
• Handling structured, semi-structured, and unstructured data
• Query optimization
• Experience with Infrastructure as Code (Terraform / CloudFormation)
• Hands‑on experience in building CI/CD pipelines for data platforms
• Exposure to containerization (Docker, ECS, EKS)
• Performance
• Production stability
• Excellent communication and stakeholder collaboration skills

4. Good-to-Have Skills

• Experience with streaming technologies (Kinesis, Kafka, MSK)
• Exposure to Lakehouse architectures and modern data platforms
• Integration with BI and analytics tools
• Knowledge of:
  • Data governance
  • Data quality frameworks
  • Metadata management
• Familiarity with FinOps (cost optimization on AWS)
• Exposure to Marketing/Customer Data Platforms (CDP / MarTech)
• Experience working in Agile delivery models with global teams

5. Key Responsibilities

Data Platform Design & Development

• Design and implement AWS-based data engineering solutions
• Build and optimize batch and streaming pipelines
• Develop:
  • SQL-driven data transformations
  • Python-based pipelines
• Scalability
• Performance

Delivery & Quality Ownership

• Own data engineering deliverables from development to production support
• Perform:
  • Performance tuning
  • Cost optimization
  • Capacity planning
• Troubleshoot complex data and production issues
• Ensure solutions meet security, reliability, and scalability standards
• Work with architects, product owners, and stakeholders
• Translate business requirements into technical data solutions
• Provide:
  • Technical estimates
  • Implementation trade-offs
• Participate in solution design and architecture discussions
• Follow coding standards and data engineering best practices
• Participate in code reviews and continuous improvement initiatives
• Ensure compliance with AWS, security, and governance guidelines

6. Education Qualification

• Bachelor’s or Master’s degree in:
  • Computer Science
  • Information Systems
  • Data Engineering
  • or related field

7. Certifications

• Preferred:
  • AWS Certified Data Analytics
  • AWS Solutions Architect

(Any two preferred)

• Plus:
  • Databricks
  • Snowflake or other cloud data platform certifications