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Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage. Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka.
$160,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Databricks Certified Associate Developer for Apache Spark Preferred. Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.
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Advanced knowledge of Apache Spark, Azure Data Factory, Azure DevOps, Azure Machine Learning, Hadoop, Hive (Apache), Informatica, Microsoft Azure, Databricks, Microsoft Power BI, Microsoft SharePoint, Microsoft SQL Server, MS Office Products or Operational Data Integration (OLTP.
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Experience with machine learning and AI-driven data processing. -Proficiency in data engineering tools and technologies such as Apache Airflow, Spark, Kafka, Hadoop, or similar.
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Data Science & Machine Learning Engineer will be responsible for developing state-of-the-art data processing pipeline based on machine learning models and leveraging data science algorithms to parse substantial data and logs in a timely manner to automatically tackle the issues or provide recommendations for the next step of solving problems.
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Examples include MPP data warehouses like Snowflake and Amazon Redshift and all-in-one Apache Spark platforms like Databricks. Examples include Azure Machine Learning Studio, Google’s Vertex AI, IBM Watson Studio, Amazon SageMaker and open-source tools like Kubeflow.
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Job Title: ETL Ab Initio Data Validation Engineer (SDET) Primary skills: Ab Initio, Pyspark, Hive Iceberg on Azure Datalake (ADLS) or Iceberg on Snowflake Database and OS: Oracle, DB2, SQL, Data Warehousing, Linux About the job We are seeking a highly skilled ETL Ab Initio Validation Engineer with expertise in Ab Initio and Snowflake or Azure Datalake to join our dynamic team within our organization.
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Ever-expanding technology like IoT, machine learning, and artificial intelligence means more structured and unstructured data is available today than ever before. 5+ years of experience with Distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka.
$220,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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What Additional Experience Makes A Strong Candidate: 4+ years of production experience with Scala 4+ years of production experience with Micro Services / Streaming Services Experience addressing ML problems, particularly in domains such as Time Series, Computer Vision (CV), Natural Language Processing (NLP), and data mining Proficiency with popular ML frameworks (Xgboost, TensorFlow, PyTorch, etc.
$275,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Expertise with Google Cloud Platform’s distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc.
$140,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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4+ Experience with technologies Apache Mesos, Marathon, or Apache Spark. Lead a portfolio of diverse technology projects and a team of developers with deep experience in machine learning, distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company.
$201,400 a yearFull-timeExpandApply NowActive JobUpdated 20 days ago - UpvoteDownvoteShare Job
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Ability to work with large datasets and experience with data processing tools like SQL, Pandas, and big data technologies such as Apache Spark. Design and implement data-driven solutions using Microsoft Azure’s tools and services, such as Azure Machine Learning, Azure Databricks, Azure Data Factory and Azure databases or Fabric implementation.
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You will leverage your expertise in Databricks, Apache Spark, and Azure to design, develop, and implement data warehouses, data lakehouses, and AI/ML models that fuel our data-driven operations.
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Establish ML Ops practices within the data engineering framework, focusing on automation, monitoring, and optimization of machine learning pipelines. You will play a crucial role in designing, building, and maintaining our data platform, with a strong emphasis on streaming data, cloud infrastructure, and machine learning operations.
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Proven experience in data engineering, with a strong focus on GCP, BigQuery, ETL, and Apache Airflow. As a Senior Data Engineer, you will play a pivotal role in shaping the company's data strategy.
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machine learning apache spark engineer data processing jobs Title: staff engineer
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