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The Sr. Manager of Data & Engineering & Analytics will lead the data Engineering team that will include build out of databricks platform, data migrations and integrations between systems at Clean Earth Inc (Division of publicly traded Enviri corporation) with 1,001-10,000 employees and close to $1bn in revenue.
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Have a good understanding of data modeling and experience with data engineering tools. As part of the team, you will work with product, engineering, and customer success teams to drive complex data and trend analyses to propose ways to improve and, thereby contributing to improve the experience.
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Full stack engineering on our SEO (search engine optimization) team means integral participation in our web framework development, iterative testing, analytics, monitoring and data science on our comprehensive datasets.
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Experience with reliability engineering specifically in areas such as data quality, data observability and incident management. This role is exciting because you will work collaboratively and communicate effectively with engineering, data science and analytics teams globally.
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Data & Storage: RDS & a standard DB entity accessing layer on top, Kinesis/Kafka, streaming infra, and observability across the tech stack. Experience with some of the key systems used at Zip, such as Elasticsearch, RDS, Celery, Redis, Kinesis/Kafka, K8s, nginx.
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Have a passion for data with exceptional analytical and critical thinking skillsWell versed with Spark, Hadoop, Cloud, Python/Scala and Java, Streaming, Kafka. Bachelor's degree in computer science or a related field plus 3 years' experience in data science, data engineering, software engineering, or related domain.
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You will help provide architectural blueprints, prototype solutions, analyze data flows, and create the necessary specs to roll out various solutions. Delivered event-driven solutions or worked with Apache Kafka.
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Plus, to be a Committer or PMC Member with the Apache Software Foundation on big data technologies: Apache Spark, Hadoop Stack, Kafka, Comet, Atlas, delta. Recognized Subject-Matter Expert big data engineering and cloud-based data warehouse.
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We are seeking a Senior Software Engineer to join Apple Services Engineering (ASE) who brings deep passion for building distributed large scale data processing systems, frameworks and platforms using big data technologies.
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Proficiency in performance-related for technologies such as Java/Scala, Spark, Kafka, PostgreSQL, NoSQL, Microservices, Batch, Micro-Batch and Data Streaming. Understanding of various performance metrics and KPIs for RDBMS, NoSQL, Kubernetes and Spark Data Engineering Engine.
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You'd be joining a team of backend-focused developers who work primarily in Golang, Kafka, Cassandra, Elasticsearch, Docker/Kubernetes, and AWS-native services, making use of distributed/micro-services based architecture to build features for our highly available cloud SaaS platform, Falcon.
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Demonstrated experience using Spring, Kafka, Elasticsearch, and NiFi. The Database Engineer in this role will perform high level database engineering and management tasks. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
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Introducing and maintaining best practices across multiple data engineering disciplines like Data Lakes/Data Warehousing and batch processing, streaming and real-time data processing, API for data-as-a-service, data quality, etc.
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2+ years of hands-on experience in developing scalable data pipeline using technologies like Kafka, Databricks, Spark and Scala applying ETL and ELT concepts. Experience with Data Management, Data Security, Data Quality, Data Governance.
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5+ years of industry experience working with batch or streaming distributed data processing technologies (e.g. Hadoop, MapReduce, Spark, Flink, Kafka, Presto, etc.) Identify common patterns and build self-serve tools to scale data engineering, and automate lifecycle of datasets with highest standards of data quality.
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