We are looking for a Data Engineer with strong backend engineering fundamentals and hands‑on experience building scalable, high‑performance data pipelines. The ideal candidate comes from a Java or Python backend background and has worked extensively with Apache Spark. Experience with Scala is a plus but not mandatory.

This role involves designing, developing, and optimizing data processing workflows, ensuring reliability, performance, and maintainability across distributed systems.

• Key ResponsibilitiesDesign and develop distributed data processing pipelines using Apache Spark.
• Build and maintain high‑performance backend services for data ingestion, transformation, and storage.
• Work with large‑scale datasets and optimize jobs for performance, scalability, and cost efficiency.
• Collaborate with data architects, analysts, and platform teams to deliver robust data solutions.
• Implement best practices for code quality, testing, CI/CD, and version control.
• Troubleshoot production issues and improve system reliability.
• Contribute to the design of data models, schemas, and storage strategies.
• Ensure data quality, lineage, and governance across pipelines.

• Required Skills & Experience3–5 years of experience as a Data Engineer or Backend Engineer.
• Strong programming skills in Java or Python (Scala optional).
• Hands‑on experience with Apache Spark (batch or streaming).
• Solid understanding of backend concepts:
• Multithreading & concurrency
• REST APIs / microservices
• Data structures & algorithms
• Performance optimization
• Experience with distributed systems and cloud‑based data platforms (AWS, Azure, or GCP).
• Familiarity with SQL and relational databases.
• Experience with version control (Git) and CI/CD pipelines.

• Good to HaveExperience with Scala for Spark development.
• Knowledge of Delta Lake, Iceberg, or Hive.
• Exposure to Kafka, event streaming, or real‑time data pipelines.
• Experience with containerization (Docker, Kubernetes).
• Understanding of data warehousing concepts.
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