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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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You will leverage workflow scheduling applications and data ingestion infrastructure that powers the analytics and data science behind our flagship streaming applications - Disney+, Hulu, ESPN+, and Star.
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Advanced knowledge of data modeling concepts and integrated application development methodology using tools like Apache Spark, Hadoop, Hive, and Apache Kafka. Our data platform provides real time streaming, batch processing and pipeline orchestration, data lake management, and data cataloging.
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Minimum of 5 years’ experience with automated delivery pipelines, agile delivery, and test-driven development and real-time data processing (e.g., Kafka, Spark Streaming). Minimum of 5 years of hands-on skills in Python, Apache Spark, Hive, Cassandra, Snowflake, Elastic, Lucene, DataBricks, Hadoop, Redshift, Scala, Java, or other relevant technologies in data engineering.
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6+ years experience in building and maintaining large-scale ETL/ELT pipelines (batching and/or streaming) that are optimized for performance and can handle data from various sources, structured or unstructured.
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In-depth knowledge of big data technologies such as Hadoop, Spark, Kafka, and cloud platforms such as AWS, Azure, GCP, Snowflake, Databricks, etc. Hands-on experience extracting (ETL using CDC, Transaction Logs, Incremental) and processing large data sets for Streaming and Batch data loads.
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Load and process disparate data sets using technologies including but not limited to PostgreSQL, Elasticsearch, Snowflake, Spark, Java, and Python. Experience with Cloud system architecture and design, large scale streaming data processing.
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Minimum of 5 years' experience working with data structures, data streaming and pipelines, search, training and inference algorithms, event driven patterns and middleware technologies. In this role, you will have the opportunity to make a lasting impact on the digital transformation journey, drive customer-centric innovation, and the ideal candidate will have a background of running transformation projects in similar disruptive industries such as Digital Payments or Fintech.
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The PaCI team works to minimize the damage of inauthentic behaviors on TikTok platforms, covering multiple classical and novel community and business risk areas such as account integrity, engagement authenticity, anti-spam, API abuse, growth fraud, live streaming security and financial safety, etc.
$177,688 - $341,734 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Experience with the Hadoop eco system, including HDFS, YARN, Hive, Pig, and batch-oriented and streaming distributed processing methods such as Spark, Kafka, or Storm. Experience with the Hadoop eco system, including HDFS, YARN, Hive, Pig, and batch-oriented and streaming distributed processing methods such as Spark, Kafka, or Storm.
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The ideal candidate has advanced Python and SQL skills, experience with distributed data processing, and has deployed models in batch, streaming, and real-time API contexts. Distributed computation (e.g., Spark, Hive, Athena.
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Experience with large scale streaming platforms (e.g. Kafka, Event Hub, Databricks Live Table / Structured Streaming), processing frameworks (e.g. Spark, Databricks) and storage engines.
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You have extensive hands-on experience in building scalable data platforms and reliable data pipelines using technologies such as Spark, ElasticSearch, Databricks, Clickhouse, AWS Kinesis, and/or Kafka.
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Experience with streaming data processing technologies such as Kafka, Flink, and/or Spark Streaming. Strong understanding of data warehousing concepts and experience with technologies such as Snowflake, Redshift, and/or BigQuery.
$120,000 - $140,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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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.) DescriptionIn this role, you will be building ultra large scale batch & streaming datasets to support analytics, experimentation and machine learning and helping to drive our self-serve strategy for reporting on-behalf of data scientists and product engineers as we collectively make product better.
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