Senior Data Architecture Specialist

1 week ago

india beBeeData Full time

Key Responsibilities Define and execute the enterprise data architecture strategy in alignment with business goals and future growth needs.The ideal candidate will have a strong background in designing and implementing large-scale data architectures, focusing on Data Lakes, Data Warehouses, and Data Lakehouses. They should be able to leverage technologies such as Snowflake, Databricks, Azure Data Factory (ADF), cloud storage, and related ecosystem services to drive modernization of data platforms. They must design and implement robust batch and streaming data pipelines using frameworks like Apache Spark, ensuring scalability, reliability, performance, and data integrity. This includes developing frameworks for data ingestion, transformation, orchestration, and automation (ETL/ELT) and real-time streaming ingestion pipelines. Additionally, they should establish and enforce data governance, metadata management, data cataloging, data lineage and quality standards across the data ecosystem. As a leader, they must provide technical guidance, mentor data engineering teams, drive best practices, and ensure maintainability of the data platform. They are also expected to stay up-to-date with emerging data technologies and industry trends, recommending and adopting improvements to continuously evolve the data platform.Required Skills & ExperienceA successful candidate will have at least 8+ years in a Data Architect/senior data-platform architect role, with overall IT experience ranging from 12-15 years. They should have proven experience in architecting and implementing Data Lakes, Data Warehouses, and Data Lakehouses at enterprise scale. Strong hands-on expertise with Snowflake (preferred), Databricks, Azure Data Factory, cloud-native storage and services is necessary. Deep proficiency in Apache Spark (batch and streaming), data ingestion frameworks, ETL/ELT workflows, and orchestration is also required. They should possess strong programming skills in Python for data engineering, automation, custom transformations, and scripting. Solid understanding of cloud-native data ecosystem preferably on Azure, AWS or GCP experience also relevant. Excellent communication, stakeholder management, cross-functional collaboration, and leadership skills are essential. Education-wise, a Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field is required. Proven track record of designing and delivering large-scale data architecture solutions in enterprise environments is mandatory.

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