Data Engineer

Data Engineer

India, Remote

Full Time / Contract

# of Positions - 3

Job Summary

The Data Engineer will be responsible for designing, developing, and optimizing scalable data pipelines and cloud-based data solutions. This role requires strong Python programming skills, expertise in ETL / ELT processes, and deep hands-on experience with AWS cloud services such as S3, Glue, Lambda, Redshift, Kinesis, and DynamoDB. The ideal candidate will excel in building serverless data architectures, designing efficient data models, and ensuring robust pipeline performance through monitoring and optimization. The position is remote within India and open for both full-time and contract engagements.

Key Skills

Data Engineering & Pipelines

ETL / ELT pipeline development using Python

Data extraction, transformation, and loading into data lakes / warehouses

Workflow orchestration and automation

Real-time and batch data processing

AWS Cloud Expertise

S3, Glue, Lambda, Redshift, Aurora

Kinesis (real-time streaming)

DynamoDB (NoSQL databases)

AWS serverless architecture design

Integration of AWS managed services for data workflows

Serverless & Automation

Building event-driven pipelines using AWS Lambda

Serverless compute optimization

Automated triggering and orchestration of data processes

Data Modeling

Designing schemas for relational databases (OLTP / OLAP)

NoSQL data modeling and storage optimization

Understanding of normalization, partitioning, indexing

Monitoring & Optimization

AWS monitoring tools (CloudWatch, CloudTrail)

Pipeline performance tuning

Cost optimization across cloud resources

Collaboration & Communication

Cross-functional collaboration with analysts, data scientists, and engineering teams

Strong problem-solving, documentation, and communication skills

Minimum Qualifications

Bachelor’s degree in Computer Science, Information Technology, Engineering, or related field (preferred).

Strong hands-on experience with Python for data engineering tasks.

Deep understanding of AWS cloud services used for data storage, streaming, ETL, and serverless compute.

Experience designing and implementing ETL / ELT pipelines .

Solid understanding of relational and NoSQL data modeling concepts.

Professional Experience Requirements

Proven experience building scalable data pipelines using Python.

Experience extracting and transforming data from diverse sources into cloud-based environments.

Hands-on experience with AWS services such as S3, Glue ETL, Lambda, Redshift / Aurora, Kinesis, and DynamoDB.

Demonstrated ability to design and implement serverless architectures.

Experience creating event-driven workflows using Lambda and other AWS triggers.

Strong experience in monitoring, debugging, and optimizing cloud-based data workflows.

Experience collaborating across data science, analytics, and engineering teams to deliver complete data solutions.