Job Description - Data Engineer (AWS) 1. Basic Information

- Job Title: Data Engineer (AWS)
- Experience: 3 to 7 Years

2. Role Overview

We are looking for a hands-on Data Engineer - AWS with 3 to 7 years of experience in developing, building, and maintaining scalable, secure, and high-performance data platforms on AWS.

This is an individual contributor role focused on data pipeline development, cloud data engineering, and analytics enablement. The candidate should have strong hands-on expertise in AWS data services, SQL, and Python, along with experience in building reliable batch and streaming pipelines in a global delivery environment.

3. Must-Have Skills 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)

- Positive 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)

Location:

DGS India - Mumbai - Goregaon Prism Tower

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Engineer (Pune)
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