Data Engineer- Pyspark

About the Client

ARAs Client is a leading technology-driven organization focused on building scalable, data-centric solutions that power business intelligence and digital transformation. The company fosters innovation, collaboration, and continuous learning, enabling teams to work on cutting-edge data platforms and enterprise-scale systems.

Role Summary

We are seeking a highly skilled Data Engineer with strong PySpark expertise to design, build, and optimize large-scale data pipelines. This role involves working with distributed data systems, ensuring high data quality, and enabling seamless data flow across platforms. The ideal candidate will combine strong technical expertise with leadership capabilities to drive data engineering best practices.

Key Responsibilities

Design, develop, and maintain scalable data pipelines using PySpark

Build and optimize ETL processes for efficient data ingestion and transformation

Ensure data quality, integrity, and governance across systems

Collaborate with cross-functional teams to define data requirements and solutions

Lead technical decision-making and contribute to architectural discussions

Troubleshoot and optimize data workflows for performance and reliability

Mentor junior engineers and promote knowledge sharing

Ensure compliance with data governance and security standards

Must-Have Qualifications

5+ years of experience in Data Engineering

Strong hands-on experience with PySpark

Experience with distributed data processing frameworks

Solid understanding of ETL processes and data integration techniques

Experience with cloud-based data platforms (AWS / Azure / GCP)

Strong problem-solving and debugging skills

Nice to Have

Experience with data warehousing solutions

Familiarity with workflow orchestration tools (e.g., Airflow)

Knowledge of big data ecosystems (Hadoop, Hive, etc.)

Exposure to real-time data processing frameworks.

Tier 2 locations preferred.