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Develop, implement, test, and operate large-scale, high-performance data structures to support analytics and reporting needs.
Job Responsibility:
Build and maintain efficient, scalable ETL/ELT pipelines to ingest, transform, and integrate data from multiple structured and unstructured sources into a unified data platform
Implement data models and architectures using best practices in relational (PostgreSQL), distributed (Databricks/Spark), and NoSQL environments, ensuring data quality, consistency, and accessibility
Partner with product, business, and software teams to gather requirements, analyze source data, and deliver solutions that enable data-driven decision-making
Design and implement data solutions that scale with growing data volumes and support high-performance querying and analytics
Produce and maintain comprehensive dataset documentation, metadata, and technical specifications to ensure transparency and reproducibility
Engage in the full development lifecycle, from requirements gathering, design, implementation, and testing, through deployment, documentation, and ongoing support
Evaluate proposed data solutions, tools, and platforms to ensure alignment with organizational standards and future scalability
Share best practices in data modeling, pipeline development, and performance optimization
Requirements:
Master's degree in Computer Science, Computer Engineering, Data Science, or Analytics
24 months of experience in the job offered or closely related technical occupation
Experience in big data engineering using Hadoop
Experience in SQL querying
Experience in building multi dimensional reports and dashboards
Experience in big data analytics using Spark and Databricks
Experience in automating API processes
Experience in Iceberg installation and implementations
Experience in working with log data such as Netflow, Cisco, Solarwinds, Palo Alto, and Systempulse