:

– 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

- Solid 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)

Location: DGS India - Mumbai - Goregaon Prism TowerBrand:

MerkleTime Type:

Full timeContract Type:

Permanent

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