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Software Engineering (AWS Cloud) - Lead II. AWS, AWS Glue, Hudi & Kafka. Familiarity with big data processing tools and techniques. Experience with streaming data platforms.
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Strong experience with big data technologies such as Spark, Kafka, Kinesis, Airflow, Hadoop, Hive, Databricks, Snowflake etc. Deploy and maintain data science, machine learning and other data products using best practices in engineering.
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Travelers Data Engineering team constructs pipelines that contextualize and provide easy access to data by the entire enterprise. Four years of data engineering or equivalent experience.
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Implement data engineering, ingestion, and curation functions on Azure cloud using Azure native programming. Experience implementing and maintaining data platform core services (data lake, data integration, data warehouse) and configuring and integrating infrastructure and PAAS (e.g., Azure Data factory, Informatica cloud services, Databricks, Data gateway and Qlik Sense, Power BI.
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Strong background in streaming data technologies and real-time data processing using kafka, spark, flink. Implement and optimize streaming data processing pipelines using Kafka, Flink.
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Our Principal Engineer is a lead member of the engineering staff working across Business Services Engineering, Data Engineering, Platform Engineering, and Infrastructure Engineering to ensure that we provide a friction-less experience to our customers, maintain the highest standards of protection and availability.
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Experience with streaming/event-based data services and tools (e.g., Kafka)Experience working with Data Vault automation tools (e.g., Vault Speed, Where Scape, coalesce.io)Experience using Informatica Data Management CloudAdvanced degree in Computer Science, Engineering, or a related field.
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Hands-on experience working with Azures PaaS offerings such as Purview, Databricks, Azure Data Lake Storage, Data Factory as well as monitoring of the environments. 2+ years of hands-on development experience with event based streaming technologies (Apache Kafka / Azure Event Hub or similar.
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If you're passionate about impactful data engineering within a collaborative, dynamic setting, we invite you to apply. 7+ years of experience (10+ preferred) in data engineering.
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Asynchronous data processing and stream processing (Kafka, Amazon Kinesis) Distributed data stores (DynamoDB, Cassandra, MongoDB) and distributed data warehouses (Redshift, BigQuery.
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Experience working with data technologies, such as Apache Spark, Kafka, dbt, and data warehouses, for handling large-scale datasets. Be the driving force behind Tecton's advocacy and help us revolutionize how engineering teams approach AI/ML productionizationJoin a talented, passionate team of AI/ML innovators, with colleagues coming from Google, Salesforce, Databricks, as well as from other innovative data startupsWork with cutting-edge technology and leave your mark on the greenfield industry of real-time MLResponsibilities.
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The ideal candidate has deep Java development experience along with a proven background in Microservices and Containers Architecture (REST API, Dockers, Kubernetes, ASB) as well as EDA (Event Driven Architecture) using Kafka.
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Designing and implementing Distributed systems like Spark, Kafka and leveraging Data Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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Knowledge of data pipeline tools and frameworks, such as Airflow, Spark, Kafka, etc. Proficiency in data engineering, ETL architecture and development, and end-to-end processes.
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Collaborating with various engineering teams, Data Science, product, and operations to develop one of the most innovative components of the product. Experience working with one of the following technologies database: ClickHouse, PostgreSQL, MongoDB, RedisExperience working with one of the following event streaming technologies technologies: RabbitMQ, Kafka, Amazon SQS,Work in a way that works for youFlexBase, Akamai's Global Flexible Working Program, is based on the principles that are helping us create the best workplace in the world.
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