About the role Join a data-driven team where your work with distributed processing helps turn complex datasets into meaningful insights. In this role, you’ll collaborate closely with engineers, analysts, and stakeholders to build reliable, scalable data solutions using Spark, contributing to faster decision-making and better customer outcomes. You’ll be encouraged to take ownership of deliverables, improve performance, and bring clarity to ambiguous problem statements through structured analysis and thoughtful implementation. If you enjoy solving large-scale data challenges, optimizing pipelines, and working in a collaborative environment that values learning and continuous improvement, this opportunity will help you grow your technical depth while making a visible impact across projects and teams. Responsibilities Key Responsibilities: • Design, develop, and maintain scalable data processing jobs using Spark for batch and/or near-real-time workloads. • Analyze large datasets to identify trends, anomalies, and data quality issues; implement validation and reconciliation checks. • Optimize Spark applications for performance by tuning partitions, caching strategies, memory usage, and execution plans. • Collaborate with cross-functional teams to translate business requirements into technical solutions and well-defined deliverables. • Implement robust error handling, logging, and monitoring to ensure reliability and easier troubleshooting. • Participate in code reviews, follow engineering best practices, and contribute to reusable components and standards. • Support deployments and production issues by performing root-cause analysis and implementing preventive fixes. Technical requirements Minimum Qualifications: • Bachelor’s degree (or equivalent) in Engineering/Technology/Computer Science or related field (BTech/BE/MSc or equivalent). • 5+ years of experience working on data engineering or big data processing initiatives. • Hands-on experience building and maintaining Spark-based data processing solutions. • Strong understanding of distributed computing concepts and data processing fundamentals. • Ability to work independently on assigned modules and collaborate effectively within a team. Preferred Qualifications: • Master’s degree (MTech/MCA or equivalent) in a relevant discipline. • Proven experience delivering end-to-end Spark pipelines, including development, testing, and production support. • Experience improving job performance and stability through Spark tuning and structured troubleshooting practices. • Familiarity with building reusable frameworks/components to standardize Spark development across projects. • Strong communication skills to explain technical trade-offs and align solutions with stakeholder expectations. Good to have skills: Hadoop, Hive, Kafka, Airflow, Delta Lake Preferred skills Technology->Data Engineering->Spark->Spark Sreaming,Technology->Java->Apache->Spark Education Bachelor of Engineering,Bachelor Of Technology