About this role
At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.
Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.
Core Skills & Experience
- Strong hands-on experience designing, building, and optimizing enterprise-scale data pipelines, data architecture, and data products for performance, scalability, and efficient AI agent consumption.
- Deep expertise in Starburst/Trino and distributed SQL, including query optimization, materialized views (MVs), data modeling, storage design, and tuning data products for low-latency, high-concurrency AI agent access.
- Strong experience optimizing data storage, partitioning, indexing, file formats, and materialization strategies to improve query performance, data freshness, and cost efficiency.
- Hands-on development experience with Apache Spark using Scala and/or Python/PySpark, including migrating complex, SQL-intensive transformations to distributed processing frameworks to improve data curation speed and efficiency.
- Strong ability to evaluate and redesign data processing workflows, selecting the appropriate technologies and execution patterns across SQL, Spark, and other processing frameworks to optimize performance.
- Experience with RDBMS technologies and modern lakehouse architecture, preferably Apache Iceberg, including table design, partitioning, compaction, and data lifecycle optimization.
- Understanding of integration patterns across NiFi, Kafka, Spark, RDBMS, and lakehouse platforms for efficient batch and real-time data processing.
- Strong software engineering practices, including CI/CD, automated testing, source control, infrastructure automation, observability, and production support.
- Experience with data governance, metadata management, lineage, data quality, and secure data access in enterprise environments.
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