Data Engineer

Role Overview

We are seeking a skilled and motivated Data Engineer with 5–8 years of experience in building scalable data pipelines using Python, PySpark, and AWS services. The ideal candidate will have hands-on expertise in big data processing, orchestration using AWS Step Functions, and serverless computing with AWS Lambda. Familiarity with DynamoDB and deployment of ETL programs in AWS is essential.

Key Responsibilities

Design, develop, and maintain robust data pipelines using Python and PySpark

Handle large-scale data processing and transformation using AWS services

Implement orchestration workflows using AWS Step Functions

Develop and manage serverless components using AWS Lambda

Deploy and monitor ETL programs in AWS environments

Configure and optimize DynamoDB for data storage and retrieval

Collaborate with cross-functional teams to understand data requirements and deliver scalable solutions

Ensure data quality, integrity, and security across all stages of the pipeline

Required Skills & Qualifications

5–8 years of experience in data engineering or related field

Strong proficiency in Python and PySpark

Solid understanding of AWS services including S3, Lambda, Step Functions, Glue, and DynamoDB Experience deploying and managing ETL workflows in AWS

Familiarity with NoSQL databases, especially DynamoDB

Knowledge of CI / CD practices and infrastructure-as-code tools (e.g., CloudFormation, Terraform) is a plus

Excellent problem-solving and communication skills

Ability to work independently in a remote setup

What We Offer

Fully remote work environment

Opportunity to work on cutting-edge data engineering projects

Collaborative and inclusive team culture

Competitive compensation and benefits