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Design and develop integration capabilities between Informatica MDM and various data sources/systems including on-premise and AWS cloud environments using IICS, Power Center, IDQ, Web services and other integration mechanisms and patterns Data modeling & Data Mappings (Landing, staging and Base Objects), Data validation, Match and Merge rules, SIF Framework and MDM User Exits.
$103,000 - $155,000 a yearFull-timeExpandApply NowActive JobUpdated 14 days ago - UpvoteDownvoteShare Job
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Hands-on experience with distributed systems Hadoop, Spark, Presto, HDP, CDH. Minimum 5+ years of experience as a hands-on solutions engineer, supporting all phases of the technical sales cycle.
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Working, hands on, knowledge of modern Data Pipeline tools (DBT, Snowflake, Airflow) and Data visualization tools (Looker) Practical hands on experience with: Building large-scale event driven systems and relevant services (Kafka, Confluent, RabbitMQ) and streaming based data pipelines.
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Demonstrated, hands-on experience with data visualization tools, e.g. Looker and Tableau. Experience working with cloud based data warehouses like Google BigQuery, Redshift, or Snowflake.
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The ideal candidate must have hands-on technical expertise developing responsive web applications, including creation of UI design mock-ups, the design of the front-end presentation layer using modern techniques and integrating with backend services and data sources in a micro-services based architecture that hosted on Google Cloud Platform (GCP.
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Experience with relevant code repository and project tools such as GitHub, JIRA and Confluence Working experience with Continuous Integration & Continuous Deployment with hands-on expertise on Jenkins, Terraform, Splunk and Dynatrace.
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We are seeking a hands-on Data Integration Engineer who has deep technical expertise in Apache Airflow, Azure Data Factory and Databricks and understands how build and scale a performant data platform and enjoys enabling other team members to do the same.
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Strong understanding or Snowflake on Azure Architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse.
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At least 5 years of experience in working as Data Architect for Enterprise DWH and BI project for both on prem and cloud with minimum of 3+ years of hands-on experience in working on Snowflake for DWH Development as Architect.
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The Afero next-generation IoT PaaS accelerates time to market, provides hardened security from device to cloud and from factory to customers, simplifies onboarding to help maximize customer connections, and uses data ontology to streamline machine learning.
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You are hands-on and have strong programming skills and background in one or more of the following AI/ML domains: deep learning, natural language processing (NLP), and analytics. Experience with machine learning platforms and distributed systems such as AWS sagemaker, Google Cloud Vertex AI, Azure ML Studio, Databricks.
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Provide hands-on support in ETL processes and data pipeline construction for automated reporting and analytical solutions. Our ultimate goal is to enable the dark warehouse, a fully automated eCommerce fulfillment center with swarms of intelligent, cloud-connected robots to enable affordable same-day fulfillment for every retailer, large and small.
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Hands-on experience designing, building, and maintaining data pipelines using tools such as Azure Data Factory, Apache Kafka, Apache Airflow, AWS Glue, Google Cloud Dataflow, and similar technologies.
$130,000 - $160,000 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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5+ years of hands-on experience in applied data science and/or economic research (real estate or housing experience preferred). Experience with cloud and big data technologies to extract and analyze data (Snowflake, S3, Redshift, Athena, Hive, BigQuery, Spark, etc.
$135,100 - $196,100 a yearExpandUpdated Today - UpvoteDownvoteShare Job
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Hands-on expertise building, deploying, and managing machine learning models on cloud platforms like Google Cloud (BigQuery, Vertex AI), AWS (SageMaker, Lambda), or Azure (Machine Learning Studio.
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