• Architect a Self-Healing, Autonomous Data Fabric. • Replace legacy ETL with a "nervous system" where metadata is active, governance is computational, and data sharing is zero-copy.

• Design and build net new resilient batch and real-time data pipelines that power critical products and drive rapid business growth. • Design and build data pipelines facilitating the migration from existing disparate data models and data sources, into new data models and databases in production. • Champion data reliability and performance by implementing proactive monitoring and quality-first engineering practices. • Optimize data models to support fast, efficient decision-making across high-impact product and business use cases. • Partner closely with product, engineering, and data science teams to quickly translate evolving business needs into scalable, production-ready data solutions. • Shape the future of data engineering at scale, helping define the long-term roadmap and setting best practices as we grow. • Coach and level up teammates, fostering a culture of learning, ownership, and continuous improvement. • Demonstrate strong self-direction and thrive in environments with high ambiguity, making sound decisions even when clear solutions are not readily apparent.

• Design, build, and maintain the internal data warehouse and analytical data layer, consolidating data from across our product and operational systems into a single reliable source of truth. • Define and enforce data models, schemas, and data contracts so that downstream consumers — data analysts and business teams — can trust and self-serve the data they work with. • Build and maintain transformation pipelines that turn raw internal data into clean, structured analytical datasets ready for BI, reporting, and AI use. • Collaborate with Data Analysts to enable AI and machine learning use cases on top of internal data — building the datasets and infrastructure they need to train models and run analytical workflows. • Implement data quality monitoring, lineage tracking, and observability across the warehouse so issues are caught early and data reliability is maintained over time. • Work with stakeholders across engineering, product, and business teams to understand their data needs and translate them into scalable, well-documented data models. • Champion good data engineering practices across the team: CI/CD for data assets, testing, documentation, and reproducibility.

Role Description Cyber Data Engineer 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 - 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. Qualifications - Experience in data engineering and analytics. - Strong programming skills in languages such as Python, Java, or Scala. - Proficiency in SQL and NoSQL databases. - Familiarity with cloud platforms (AWS, Azure, GCP). - Ability to work with big data technologies (Hadoop, Spark). Requirements - Bachelor's degree in Computer Science, Engineering, or a related field. - 3+ years of experience in a data engineering role. - Strong analytical and problem-solving skills. - Excellent communication and teamwork abilities. Benefits - Competitive salary and performance-based bonuses. - Comprehensive health and wellness benefits. - Flexible working hours and remote work options. - Opportunities for professional development and growth. - Employee recognition programs.