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.