ETL Data Engineer

ETL Data Engineer

Skill to Evaluate: ETL, Etl Developer, GCP, Big Data, Bigquery, Kafka, Hive, Data Modeling, Python, Pyspark, SQL

Experience: 5 to 6 Years

Location: Lower Parel, Mumbai (3 Days WFO)

BGV: Education, Address, Employment, Criminal

About the Role

We are looking for a passionate and experienced Data Engineer to join our team and

help build scalable, reliable, and efficient data pipelines on cloud platforms like

Primarily on Google Cloud Platform (GCP) and secondary on Amazon Web

Services (AWS). You will work with cutting-edge technologies to process structured

and unstructured data, enabling data-driven decision-making across the

organization.

Key Responsibilities

 Design, develop, and maintain robust data pipelines and ETL/ELT workflows

using PySpark, Python, and SQL.

 Build and manage data ingestion and transformation processes from various

sources including Hive, Kafka, and cloud-native services.

 Orchestrate workflows using Apache Airflow and ensure timely and reliable

data delivery.

 Work with large-scale big data systems to process structured and

unstructured datasets.

 Implement data quality checks, monitoring, and alerting mechanisms.

 Collaborate with cross-functional teams including data scientists, analysts,

and product managers to understand data requirements.

 Optimize data processing for performance, scalability, and cost-efficiency.

 Ensure compliance with data governance, security, and privacy standards.

Required Skills & Qualifications

 5+ years of experience in data engineering or related roles.

 Strong programming skills in Python and PySpark.

 Proficiency in SQL and experience with Hive.

 Hands-on experience with Apache Airflow for workflow orchestration.

 Experience with Kafka for real-time data streaming.

 Solid understanding of big data ecosystems and distributed computing.

 Experience with GCP (BigQuery, Dataflow, Dataproc

 Ability to work with both structured (e.g., relational databases)

and unstructured (e.g., logs, images, documents) data.

 Familiarity with CI/CD tools and version control systems (e.g., Git).

 Knowledge of containerization (Docker) and orchestration (Kubernetes).

 Exposure to data cataloging and governance tools (e.g., AWS Lake

Formation, Google Data Catalog).

 Understanding of data modeling and architecture principles.



Soft Skills

 Strong analytical and problem-solving abilities.

 Excellent communication and collaboration skills.

 Ability to work in Agile/Scrum environments.

 Ownership mindset and attention to detail.