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Description:
Lead the design, development, and optimization of complex data systems, including data flows, lakes, warehouses, and ETLs, across various cloud platforms.
Architect and manage Elasticsearch clusters, focusing on performance optimization, security, and scalability.
Create and optimize indices for performance and storage efficiency, leveraging index templates and mappings.
Build and optimize Elasticsearch queries using the Query DSL, including complex aggregations and full-text search.
Implement data ingestion pipelines to load data from various sources, including relational databases (RDBMS), into Elasticsearch.
Oversee performance monitoring, cluster management, security protocols, backup and restore procedures, and version management of Elasticsearch clusters.
Develop and maintain data pipelines using industry-standard tools such as Apache Spark, Apache Hive, Apache Airflow, and Stitch.
Implement data pipeline observability strategies to ensure reliable data flow and system transparency.
Apply modern software development practices, including Agile methodologies, Test-Driven Development (TDD), and Continuous Integration/Continuous Deployment (CI/CD), to enhance data engineering workflows.
Requirements:
A minimum of 5 years of experience as a technical lead, building and extending complex data systems.
Advanced proficiency in Python, with at least 3 years of experience.
Strong SQL writing and tuning skills.
Expert-level knowledge of Elasticsearch, including configuration, administration, and optimization techniques.
Demonstrated experience with building and maintaining data pipelines using tools such as Spark, Hive, Airflow, and Stitch.
Proven experience with data pipeline observability strategies and tools.
Strong understanding of software development practices, including Agile, TDD, and CI/CD.
Benefits:
Competitive salary and benefits package;
Flexible remote work arrangements;
Opportunities for professional development and growth;