• Lead the design, maintenance, and evolution of conceptual and logical information models that enable shared understanding of core business concepts across the organisation. • Own and contribute to central information architecture artefacts such as the Enterprise Data Model and Data Domain Map, ensuring alignment across domains and initiatives. • Lead definition and adoption of business concepts, taxonomies, and semantic standards. • Collaborate closely with business stakeholders, data product teams, solution architects, and engineers to translate business needs into clear information models that guide downstream implementations. • Ensure alignment between information architecture, data governance policies, and quality expectations. • Assess as‑is and define target‑state information flows supporting interoperability and lineage. • Set guardrails and best practices for information modelling across initiatives. • Guide teams in applying enterprise information models in local delivery contexts. • Promote reuse‑first and business‑aligned modelling principles. • Educate, guide, and coach stakeholders and architects, helping to embed information architecture thinking across teams. • Mentor architects and contribute to capability building across the architecture community. • Contribute to continuous improvement of architecture tooling and ways of working, including integration with data catalogues and metadata management tools.

Role Description We're looking for a passionate Data Engineer II to help build and scale the data platform that powers analytics, product insights, and data-driven decision making across the organization. You'll work on modern data technologies including Kafka, Maxwell, Spark, S3, Trino, BigQuery, dbt, and Metabase to build reliable, scalable, and high-performance data systems. From real-time data ingestion and distributed data processing to dimensional modeling and data platform development, you'll play a key role in shaping the foundation of our data ecosystem. As part of the Data Engineering team, you'll: - Design and build robust data pipelines. - Implement modern data lake architectures. - Develop reusable data models. - Create trusted datasets that empower Product, Analytics, Data Science, and Business teams. - Contribute to improving our engineering standards by focusing on performance, reliability, observability, automation, and developer experience. This role is ideal for someone who enjoys solving complex data engineering challenges, building scalable platforms, and continuously improving how data is collected, transformed, and consumed across the organization. If you're excited about building modern data platforms, working with large-scale distributed systems, and having a meaningful impact on the company's data strategy, we'd love to hear from you. Qualifications - 4+ years of hands-on experience designing and building scalable data platforms, data lakes, and data warehouses. - Strong proficiency in Spark (Scala, python) and SQL, with experience building production-grade data pipelines and distributed data processing applications. - Hands-on experience with Apache Spark and a solid understanding of distributed data processing, performance tuning, and optimization. - Experience building batch and streaming data pipelines using technologies such as Kafka, CDC/Maxwell, or similar event-driven architectures. - Strong understanding of modern data lake architectures, including Medallion Architecture, data modeling, partitioning, and storage optimization. - Experience working with cloud-native data platforms and technologies such as Amazon S3, BigQuery, Trino, or similar analytics engines. - Solid experience designing dimensional models, star schemas, and building reliable data marts that support analytics and business intelligence. - Hands-on experience with dbt, including developing reusable models, implementing automated testing, and maintaining documentation. - Strong knowledge of data quality, observability, lineage, and engineering best practices to build reliable and maintainable data products. - Experience optimizing large-scale data pipelines, SQL queries, and distributed processing jobs for performance, scalability, and cost efficiency. - Familiarity with CI/CD, Git-based development workflows, infrastructure automation, and modern software engineering best practices. - Excellent problem-solving skills with the ability to independently own projects from design through production. - Strong communication and stakeholder management skills, with experience collaborating across Product, Engineering, Analytics, and Business teams. - A passion for building scalable data platforms and continuously improving developer experience, platform reliability, and operational excellence. Requirements - Design, build, and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark, and dbt to power analytics and business-critical applications. - Develop and optimize data models following Medallion Architecture (Bronze, Silver, Gold) to create reliable, reusable, and high-quality datasets. - Build and maintain cloud-native data platforms using S3, Spark, Trino, and BigQuery, ensuring scalability, reliability, and cost efficiency. - Design robust data ingestion frameworks leveraging CDC (Maxwell), Kafka, and event-driven architectures to support near real-time data processing. - Create, optimize, and maintain data warehouses and data marts that enable fast, reliable reporting and self-service analytics. - Partner closely with Product Managers, Data Analysts, Backend Engineers, and Business stakeholders to translate business requirements into scalable data solutions. - Develop reusable dbt models, testing frameworks, and documentation to improve data quality, governance, and developer productivity. - Optimize Spark jobs, Trino queries, and storage layouts for performance, reliability, and cost efficiency. - Own the end-to-end lifecycle of critical data pipelines, ensuring high availability, monitoring, SLA adherence, and proactive incident resolution. - Build and enhance the core data platform by developing reusable frameworks, automation, CI/CD pipelines, and engineering best practices. - Ensure data quality through validation, monitoring, lineage, and observability while implementing best practices for security and governance. - Enable analytics teams by delivering trusted datasets, semantic models, and dashboards that power decision-making through Metabase. Benefits - Inclusive and Diverse Environment: We foster an inclusive and diverse workplace that values innovation and offers remote environments. - Competitive Compensation: Our compensation packages are highly competitive and include potential share options for certain roles. - Personal Growth and Development: We are committed to your personal and professional growth, providing regular training and an annual learning stipend to help you advance your career in a dynamic environment. - Autonomy and Mentorship: You'll enjoy a high degree of autonomy in your role, supported by mentorship and ambitious goals that pave the way for both your success and the company's growth.

Role Description The DE&A - Core - Cloud Data Engineering role is a critical position within Zensar Technologies, responsible for driving cloud-based data engineering initiatives. The successful candidate will lead a team of data engineers, ensuring the efficient and effective delivery of projects. With a focus on Azure, they will also have experience with other cloud platforms, enabling them to provide diverse solutions to clients. Qualifications - Experience in cloud-based data engineering. - Proficiency in Azure and other cloud platforms. - Leadership experience in managing data engineering teams. Requirements - Strong understanding of data engineering principles. - Ability to deliver projects efficiently and effectively. - Experience with diverse cloud solutions. Benefits - Inclusive workplace culture. - Opportunities for personal and professional growth. - Commitment to employee well-being.

• Design, build, and maintain enterprise data architectures supporting Supply Chain execution and performance measurement • Develop data models, pipelines, and integration frameworks • Lead data migrations, conversions, and performance optimization efforts • Oversea testing, deployment, maintenance, and continuous improvement of data systems • Ensure adherence to data quality, security, and compliance standards