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Undergraduate degree with 10+ years of Data Engineering experience with specific ETL Tools (e.g., DBT, Informatica, Data Stage, etc.) Bachelor of Engineering degree with 5-10+ years of Data Engineering experience with pertinent ETL tools (e.g., DBT, Informatica, Data Stage, etc.
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Experience with data processing and ETL tools such as Apache Beam, Spark, or similar technologies, and familiarity with workflow orchestration tools like Airflow. Stay current with the latest technologies and trends in data engineering, GCP, AI, and ML, applying best practices to our data infrastructure.
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We are a multidisciplinary team with expertise in machine learning, data engineering, and software development. 3+ years of experience of working with DataBricks, SnowFlake, or other data warehouse and data lake products and technologies.
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Experience with the Azure Services and Data components: Container based solutions, Microservices, Streaming Solutions, Python & Spark Compute, Spark SQL, Azure Integration services, and ingestion tools such as ADF, Snowflake, ADL, and GenAI Cognitive capabilities.
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More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
$82,500 - $146,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience working in Autonomous Vehicle testing, evaluation, and data engineering. Experience with modern data processing pipelines and technologies (i.e. Hadoop, Spark, Airflow.
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Solid experience using distributed data processing such as Spark, BigQuery or Apache Beam. You will architect, build and maintain large-scale distributed systems to support the whole pipeline including data collection, feature engineering, model training, model evaluation, model deployment, and real-time serving.
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Hands-on experience with big data technologies such as Hadoop, Spark, or Kafka. Proven experience as a Data Engineer or in a similar data engineering role. Staying updated with the latest trends and advancements in data engineering and recommending innovative solutions to enhance data infrastructure.
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The VP, Engineering will l ead scaling the global data and software engineering team s in managing the foundational enterprise data platform and creat ing tools and frameworks that enhance data collection and processing, analytics and artificial intelligence , data dissemination , and governance.
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As part of this team, you will work on diverse Big data and Streaming Data technologies such as Spark, Iceberg, Kafka, Datazone, DynamoDB, Glue and various other AWS technologies to build a scalable and robust near-real-time data pipelines and products.
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Data platform: Hadoop, Hbase, OpenSearch, Kafka, Spark, Flink, Go, Java, SQL, Databricks, Snowflake, BigQuery. Experience with big data and workflow management technologies like Hadoop, Spark, Redshift, Athena, Airflow, etc.
$145,000 - $190,000 a yearExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Degree required, preferably in Data Science, Mathematics, Engineering, Physics, Statistics, or Computer Science required. installing, configuring, maintaining, and troubleshooting Docker, Kubernetes, Microservices Architecture, Solr, OpenSearch, Hadoop, Spark, Databricks, Airflow, NiFi.
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Experience using big data technologies and distributed computing engines, e.g. HDFS, Spark, Kafka, Cassandra, Solr, Dask. Translate business objectives into data and analytics solutions and, translate results into business insights using appropriate data engineering and data science applications.
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Primary Skill: Data Engineering. Additional Skill(s): Kafka, Python, Scala, Apache Spark. Infogain is a human-centered digital platform and software engineering company based out of Silicon Valley.
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You have a deep understanding of data modeling and experience with data engineering tools and platforms such as Kafka, Spark, and Hadoop. Advocate your data engineering expertise to the broader tech community outside of Thoughtworks, speaking at conferences and acting as a mentor for more junior-level data engineers.
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