Role Overview

We are seeking a skilled Data Engineer with 2+ years of
experience in building and maintaining scalable data solutions. The ideal
candidate will have hands -on experience across data engineering, data analysis,
and visualization, with strong programming fundamentals and the ability to work
independently in a remote environment.

Key Responsibilities

- Design,
develop, and maintain ETL pipelines using PySpark, Apache Airflow, and
Azure Data Factory (ADF).

- Build
and optimize distributed data processing jobs using PySpark.

- Orchestrate
and schedule workflows using Apache Airflow.

- Develop
and manage data ingestion and transformation pipelines in Azure Data
Factory.

- Write
clean, efficient, and reusable code using Python .

- Develop
and optimize complex SQL queries for MySQL and PostgreSQL databases.

- Work
with MongoDB for handling semi -structured and unstructured data.

- Perform
data analysis using Pandas and NumPy to support business insights.

- Create
basic to intermediate data visualizations using Matplotlib, Power BI, and
Streamlit.

- Monitor
data pipelines, troubleshoot issues,



and ensure data quality and
performance.

- Collaborate
with cross -functional teams including analysts, data scientists, and
product teams.


Required Skills & Qualifications

Programming Languages

- Proficiency
in Python


Databases

- Strong
experience with MySQL and PostgreSQL

- Hands -on
exposure to MongoDB


Data Engineering

- Experience
building ETL/ELT pipelines

- Hands -on
with PySpark for large -scale data processing

- Experience
using Apache Airflow for workflow orchestration

- Experience
with Azure Data Factory (ADF)


Data Analysis & Visualization

- Hands -on
experience with Pandas and NumPy

- Ability
to create visualizations using Matplotlib

- Experience
with Power BI for dashboards and reporting

- Exposure
to Streamlit for building data -driven applications


Positive to Have

- Experience
with Azure data services (Azure Data Lake, Synapse Analytics).

- Familiarity
with Git and version control best practices.

- Basic
understanding of data warehousing concepts.

- Exposure
to cloud -based deployments and CI/CD pipelines.