Data Engineer

0-2 years of experience in Data Engineering or a related role.

Strong understanding of Python for data processing.

Hands-on experience or familiarity with Apache NiFi.

Basic to intermediate knowledge of PostgreSQL.

Exposure to Apache Spark for large-scale data processing.

Understanding of Apache Iceberg and data lake concepts.

Experience or familiarity with Apache Airflow for workflow orchestration.

Knowledge of Hive and SQL-based data querying.

Understanding of ETL processes and data pipeline design.

Awareness of metadata management concepts.

Job Description :

Job Summary

We are looking for a motivated Junior/Mid-Level Data Engineer with a strong foundation in data engineering concepts and hands-on experience with modern data tools. The ideal candidate should be eager to learn, capable of building and maintaining data pipelines, and able to communicate effectively with cross-functional teams.

Key Responsibilities

Design, develop, and maintain ETL/ELT data pipelines.

Work with Python to process, transform, and validate data.

Build and manage data workflows using Apache NiFi and Apache Airflow.

Develop and optimize data processing jobs using Apache Spark.

Manage and query data using PostgreSQL, Hive, and Iceberg tables.

Ensure data quality, integrity, and reliability across pipelines.

Understand and work with metadata management and data cataloging concepts.

Troubleshoot data issues and support operational data workflows.

Collaborate with engineering, product, and support teams to understand data requirements.

Document data pipelines, processes, and best practices.

Nice to Have

Experience working with cloud-based data platforms.

Familiarity with data reconciliation, monitoring, and alerting.

Understanding of data governance and data quality frameworks.

Exposure to financial or trading data is a plus.

Soft Skills

Strong problem-solving mindset.

Willingness to learn and adapt to new technologies.

Good verbal and written communication skills.

Ability to work independently as well as in a team.

Role-Based Capability Development

Through this role, candidates will develop hands-on proficiency in building and maintaining production ETL/ELT pipelines, Python-based data transformation and validation, and workflow orchestration using Apache Airflow. They will gain practical experience with Apache Spark for large-scale data processing, Apache Iceberg and data lake architecture, and multi-engine SQL across PostgreSQL and Hive. Cloud data infrastructure exposure primarily AWS S3, Glue, EMR, EKS alongside metadata management, data cataloging, and cross-functional collaboration will further round out their technical and professional profile.

This role serves as a strong entry point into the data engineering career track. With consistent performance and growth, candidates typically progress toward Mid-Level and Senior Data Engineer responsibilities within 2-4 years, taking ownership of more complex pipeline designs, performance optimization, and independent technical delivery. Beyond that, experience in this role also opens parallel paths into Analytics Engineering for those inclined toward data modeling, or Data Platform Engineering for those drawn toward infrastructure and tooling.