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Expertise in data storage technologies such as relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), data warehouses (e.g., Snowflake, Redshift), and data lakes (e.g., Amazon S3, Azure Data Lake Storage.
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Strong programming skills in languages such as Python, Java, or Scala, with experience in data processing frameworks like Apache Spark or Apache Flink. Data Processing Frameworks: Lead the implementation and optimization of data processing frameworks and technologies, such as Apache Hadoop, Apache Spark, and Apache Flink, to enable efficient data processing and analysis.
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Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and services like AWS Glue, Azure Data Factory, or Google Dataflow. 8+ years of experience in data engineering or systems engineering, with a focus on data systems technologies.
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Data Governance: Establish and enforce data governance policies and procedures to ensure data quality, security, and compliance with regulatory requirements. Performance Optimization: Lead efforts to optimize data systems performance through indexing, partitioning, and other techniques, ensuring scalability and responsiveness for analytical and reporting needs.
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Proficiency in data storage technologies such as relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), data warehouses (e.g., Snowflake, Redshift), and data lakes (e.g., Amazon S3, Azure Data Lake Storage.
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Data Processing Frameworks: Implement and optimize data processing frameworks and technologies, such as Apache Hadoop, Apache Spark, and Apache Flink, to enable efficient data processing and analysis.
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Position Overview: As the Lead Data Systems Engineer, you will play a pivotal role in leading our data systems engineering initiatives and driving the design, implementation, and optimization of our data systems infrastructure.
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5+ years of experience in data engineering or systems engineering, with a focus on data systems technologies. Performance Optimization: Optimize data systems performance through indexing, partitioning, and other techniques, ensuring scalability and responsiveness for analytical and reporting needs.
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Data Systems Architecture: Design and implement scalable and reliable data systems architecture to support the organization's data needs, including data storage, processing, and analytics.
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Data Storage Solutions: Architect and implement data storage solutions, including relational databases, NoSQL databases, data warehouses, and data lakes, ensuring optimal performance, reliability, and scalability.
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Data Integration: Lead efforts to integrate data from diverse sources and systems into data systems infrastructure, ensuring data consistency, integrity, and security. Strong understanding of data integration concepts and techniques, with experience integrating data from diverse sources and systems in data systems infrastructure.
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Data Integration: Integrate data from diverse sources and systems into data systems infrastructure, ensuring data consistency, integrity, and security. Strong understanding of data integration concepts and techniques, with experience integrating data from diverse sources and systems.
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Apply now to join our team and be part of the data revolution! We're committed to building cutting-edge data systems that enable efficientdata management, processing, and analysis. Proven leadership experience, with a track record of successfully leading data systems engineering teams and delivering complex projects.
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Join us and lead our efforts in shaping the future of data systems engineering. Data Systems Architecture: Lead the design and implementation of scalable and reliable data systems architecture to support the organization's data needs, including data storage, processing, and analytics.
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Proficiency in programming languages such as Java, Python, or Scala, and experience with big data processing frameworks such as Apache Hadoop, Apache Spark, or Apache Flink. Familiarity with big data storage solutions and databases, including HDFS, Cassandra, MongoDB, Amazon S3, and Google Cloud Storage.
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