Data Engineer

Job Summary

We are looking for 4-5 years of experience as a Data Engineer with strong expertise in Spark (PySpark), SQL, and data pipeline architecture. The role involves designing, building, and optimizing scalable data workflows to support analytics and real-time insights. The ideal candidate is hands-on, detail-oriented, and enjoys building reliable data solutions while collaborating with cross-functional teams.

Job Requirements

• Strong expertise in Spark (PySpark) and SQL
• Experience in designing and building data pipelines
• Understanding of AI concepts
• Good communication and collaboration skills
• Experience with streaming tools like Kafka/Kinesis
• Proficiency in Python, with exposure to Databricks or Azure
• Familiarity with Airflow/NiFi, Debezium, HTAP systems, or logistics domain is a plus

Job Responsibilities

• Design and architect scalable data pipelines for batch and real-time processing.
• Develop and optimize data solutions using Spark (PySpark) and SQL.
• Ensure data pipelines are reliable, maintainable, and well-tested.
• Work closely with stakeholders to understand business requirements and translate them into data solutions.
• Collaborate with cross-functional teams to ensure data quality, availability, and performance.
• Stay updated with emerging tools and best practices in data engineering.

Benefits

• Innovative Engineering: Collaborative, fail-fast, flat hierarchy. Fosters learning, initiative, curiosity.
• Masterful Development: Emphasizes clean code, SOLID principles, TDD/BDD. Utilizes robust CI/CD and polyglot engineering.
• Continuous Growth: Structured mentorship, masterclasses, Geeknights, workshops, continuous skill enhancement, blog contributions.
• Agile & Client-Centric: Adopts Agile (Scrum, XP), promotes project ownership and deep client understanding for impactful solutions.
• Supportive Environment: Healthy work-life balance, flexible schedules, comprehensive benefits (generous leave), strong team-building.