Job Description

About the Role

We are looking for a

Full Stack Data Engineer

to design, build, and maintain scalable data platforms and pipelines. The ideal candidate has strong hands-on experience across data ingestion, transformation, orchestration, and cloud-based analytics, with a focus on modern lakehouse architectures.

Key Responsibilities

Design, develop, and maintain end-to-end data pipelines using

Python, PySpark, and SQL

Build and optimize data transformation workflows using

dbt on Snowflake

Develop scalable

lakehouse architectures

for structured and semi-structured data Implement reliable data ingestion frameworks using

Kafka, AWS Glue, and custom connectors

Orchestrate workflows and manage dependencies using

Apache Airflow

Manage cloud infrastructure on

AWS

(S3, Glue, EMR, Redshift/Snowflake integrations) Implement Infrastructure as Code (IaC) using

Terraform

Collaborate with cross-functional teams to deliver analytics-ready datasets Ensure data quality, performance optimization, and cost efficiency Use

GitLab

for version control, CI/CD, and collaborative development Monitor, troubleshoot, and resolve data pipeline issues in production environments

Requirements

Required Skills & Qualifications

4+ years

of experience with

AWS

data services and cloud-based data engineering Strong programming skills in

Python

and

PySpark

Hands-on experience with

Snowflake

and

dbt

for data modeling and transformations Solid understanding of

SQL

for complex analytical queries Experience with

Apache Airflow

for workflow orchestration Proficiency in

Kafka

for real-time/streaming data ingestion Experience with

AWS Glue/ Airflow

for ETL development Experience with

Terraform

for infrastructure automation * Strong experience with

GitLab

and CI/CD pipelines