The Cloud Data Architect Lead Specialist Engineer will be responsible for designing, implementing, and optimizing cloud-based data architecture and solutions that support enterprise data platforms. This role involves working closely with IT leadership, cross-functional engineering teams, and business stakeholders to define scalable data frameworks, data governance strategies, and modern data integration approaches. The candidate is expected to provide technical leadership and guidance, drive best practices, and support enterprise data transformation initiatives.
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Architect, design, and implement scalable cloud-based data platforms and solutions for enterprise analytics, data warehousing, and real-time data pipelines.
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Lead the development of data architecture standards, data models, reference architectures, and integration patterns.
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Develop and maintain cloud data frameworks using modern technologies such as Azure, AWS, or Google Cloud.
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Oversee end-to-end data lifecycle including ingestion, transformation, metadata management, security, and data quality.
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Collaborate with engineering teams, architects, and business stakeholders to define requirements and deliver data-driven solutions.
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Guide modernization efforts migrating legacy systems to cloud-native architectures.
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Provide technical leadership and mentorship to data engineers and developers.
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Ensure solutions meet performance, scalability, reliability, and security requirements.
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Conduct performance tuning and optimization for large-scale distributed systems.
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Establish best practices for CI/CD, DevOps, and Infrastructure-as-Code related to data platforms.
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Support data governance and compliance including data cataloging, lineage, and access control.
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Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field.
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Minimum 12+ years of experience in data engineering, data architecture, or enterprise data systems.
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Proven experience architecting cloud-native data platforms using AWS, Azure, or Google Cloud.
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Strong expertise in big data and distributed processing technologies such as Spark, Databricks, Hadoop, Kafka, or Flink.
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Hands-on experience with relational and NoSQL databases including SQL Server, PostgreSQL, MongoDB, or Cassandra.
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Strong proficiency in data lake, data warehouse, and ETL/ELT design patterns.
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Experience with data modeling, canonical data structures, and metadata frameworks.
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Strong programming and scripting skills in Python, Scala, or Java.
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Experience with DevOps tools, CI/CD pipelines, Terraform, CloudFormation, or Kubernetes.
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Deep understanding of data security, compliance, and governance processes.
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Excellent communication, leadership, and stakeholder engagement skills.