Data Engineer (PySpark/Airflow/Databricks)

Overview We are looking for a Data Engineer to design and build scalable data solutions in a cloud-based environment. This role requires strong expertise in distributed data processing, data warehousing, and modern data tools, along with the ability to work closely with clients and global teams. Key Responsibilities • Design, develop, and maintain scalable data pipelines using PySpark and Python • Work with large datasets in distributed environments (Databricks, AWS) • Optimize data processing and performance across platforms such as Databricks and Kafka • Implement and maintain data models and data warehousing solutions • Orchestrate workflows using tools like Airflow • Collaborate with client stakeholders and offshore teams to deliver high-quality data solutions Required Qualifications • Strong experience with PySpark, SQL, Python, and AWS • Hands-on experience with Databricks and workflow orchestration tools (e.g., Airflow) • Solid understanding of data warehousing and data modeling concepts • Experience with Kafka, dbt, and performance optimization • Familiarity with MPP data warehouses (e.g., Redshift) and version control tools (Git) • Strong communication skills and experience in client-facing environments • Proven ability to collaborate with offshore teams Preferred • Experience in the financial services domain