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8+ years of experience in data engineering, with a strong background in Apache Spark and cloud-based databases such as Snowflake. Utilize tools like Airflow for workflow management and Kubernetes for container orchestration to ensure seamless deployment, scaling, and management of data processing applications.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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Strong hands-on skills in sourcing, cleaning, manipulating, and analyzing large volumes of data using distributed computing platform (Python, R, SQL, Spark, Hive etc. You’ll make an impact by: Processing complicated and large-scale datasets using distributed computing platform, extracting insights from data, predicting future trends, and optimizing business metrics.
$160,000 - $230,000 a yearFull-timeExpandApply NowActive JobUpdated Today - 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 engineering big-data solutions using technologies like EMR, S3, Spark, Databricks.
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Streaming & Batch Data Processing (Kafka, Flink, Spark) Architect the design and implementation of our next-generation data platform and reference patterns following data mesh principles, primarily focused on provisioning, support, and governance of large-scale databases, data lakes, as well as streaming and batch processing infrastructure and pipelines.
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Must have a strong background in one or more of the following: Mathematical, Statistics, Probability, Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, Recommendation Systems, Pattern Recognition, Large Scale Data Mining or Artificial Intelligence.
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PhD degree, or foreign equivalent, in Electrical Engineering, Computer Science, Statistics or Bioengineering or a related field plus 3 years as a Data Scientist or any job title/occupation/position involving Data Sciences.
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Proficiency in a variety of technologies such as SQL, Bash, Python, Java, Presto, Spark, AWS, DataOps, DevSecOps, data streaming like Kafka, RabbitMQ, data stacks like Airflow, Databricks, dbt, and data stores like Snowflake, Cassandra, Aurora.
$165,800 - $265,625 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. 3+ years of experience creating software for retrieving, parsing and processing structured and unstructured data.
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The ideal candidate will have a strong background in data engineering and be proficient in using the ELK (elastic, kibana, logstash) stack and Databricks for data processing and analytics.
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Designed data warehouse and data lake solutions along with data processing Pipeline using PySpark using Databricks. Architect the Unity Catalog to provide centralized access control, auditing, lineage, and data discovery capabilities across Databricks workspaces.
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Experience creating software for retrieving, parsing, and processing structured and unstructured data. Experience with data warehousing, including AWS Redshift, MySQL, or Snowflake.
$58,400 - $133,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Hands-on experience with data flow and pipeline frameworks such as Kafka, NiFi, Spark, Airflow, and Hadoop. Knowledge and experience across data management domains, including data science, data governance, metadata management, data quality, business intelligence, data security & privacy, and data strategy.
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Role: A Senior Data Engineer will use his or her hands-on expertise in Spark/Hadoop, Scala programming, Linux scripting, AWS, DevOps and API technologies to expand and optimize architecture, data flows, infrastructure, APIs and data pipelines to support the Epsilon PeopleCloud Discovery Platform.
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5+ years of experience with Distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. 5+ years of experience with creating software for retrieving, parsing, and processing structured and unstructured data.
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