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Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake. Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake.
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Two plus years working with one or more of the technologies: Kafka, API Gateway, Kinesis, Docker, Apache Airflow, and/or Lambda. Certifications in AWS DevOps, AWS RDS/DMS, data engineering, data science, automation, or database technologies a plus.
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL. Utilize programming languages like Python and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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Experience in one of the public cloud providers – GCP, Azure, AWS.Experience with relational SQL and NoSQL databases, including Postgres and MongoDB.Experience with workflow management tools: Airflow, AWS data pipeline, Google Cloud Composer, etc.
$77,400 - $160,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQLAmazon Web Services (Security Groups, Batch, Lambda, SSH, S3, ECS, EC2, ALB, RDS, IAM, SNS, Fargate, ElastiCache, Redis.
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2+ years of experience with Airflow. 2+ years of experience with Presto or Snowflake. At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI.
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At least 6 years of experience in software engineering (Internship experience does not apply) New York City (Hybrid On-Site): $201,400 - $229,900 for Lead Software Engineer. Lead Software Engineer, Full Stack (Enterprise Data.
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Certifications in AWS DevOps, AWS RDS/DMS, data engineering, data science, automation, or database technologies are a plus. Postgres, Oracle, Sql Server, Snowflake, Amazon RDS preferred. 1-2 years of working experience with scripting languages, and/or statistical tools: Python, R, Scala, SAS.
$130,000 - $150,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Deployment: Airflow, Docker, Kubernetes, Jenkins, AWS. Dog-friendly office (CAM office) Deployment: Airflow, Docker, Kubernetes, Jenkins, AWS. Kensho is a Machine Learning (ML) and Natural Language Processing (NLP) company, centered around providing cutting-edge solutions to meet the challenges of some of the largest and most successful businesses and institutions.
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Knowledge of DBT, Airflow, Ansible, Terraform, Argo, Helm, or other data pipeline systems; ideally experience building and maintaining a data warehouse and understanding of simple data science workflows and terminology.
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Experience with Databricks, Airflow, Docker, and Terraform is a plus. We're looking for someone who has a passion for building scalable machine learning and MLOps platforms using technologies such as Python, Databricks, Spark, AWS, Airflow, Terraform, etc.
$110,000 - $142,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark. Experience with graph-based data workflows such as Apache Airflow.
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CI/CD orchestration (Jenkins, CircleCI, or TravisCI)ETL, data modeling, cloud-based data storage, processingGCP Data Services (Dataflow, BigQuery, Dataproc)Workflow and data pipeline orchestration (Airflow, Oozie, Jenkins, etc)Container orchestration technologies (Kubernetes, OpenStack, Docker swarm, etc)Open source software involvementMonitoring/Logging with technologies like Splunk, ElasticSearch, Logstash/Fluentd, Stackdriver, Time-series databases like InfluxDB etc.
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5+ years orchestrating and implementing pipelines with workflow tools like Databricks Workflows, Apache Airflow, or Luigi 5+ years of experience prepping structured and unstructured data for data science models.
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In-depth experience with cloud technologies such as Snowflake, dbt, AWS, Airflow. Experience with modern data tools such as Spark, Kubernetes, Docker. Experience with Docker/Kubernetes.
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