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Responsible for day-to-day Onshore - Offshore service delivery operations for Big Data, Cloud, Middleware Integration, SNOWFLAKE, Databricks applications. Experience with Customer support service delivery for Integration Big Data, Cloud, Middleware Integration, SNOWFLAKE, Databricks technology environments.
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Experience with some of the following: Kubernetes, Docker, Helm Charts, Hadoop, Cloudera, Hortonworks, Apache Storm, Apache Spark, HBase, YARN, map-reduce, big-data analytics, semantic-web (RDF, OWL), Hive, Elastic Search, MS SQL Server Big Data, and graph-databases.
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Big data – Hadoop eco-system, Distributions like Cloudera / Hortonworks, Pig and HIVE. Data integration – ETL tools like Talend and Informatica. Senior ETL engineers need to implement data integration routines using Talend technology.
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Cerner, Meditech, CPSI, Allscripts, EPIC, McKesson, NextGen data integration experience a plus. Applicants should also have a demonstrated understanding and experience using software and tools including big data tools like Kafka, Spark; relational SQL databases including Big Query or equivalent; workflow management and pipeline tools such as Airflow, and object function/object-oriented scripting languages including Scala, C.
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May recommend data analytics strategies including enterprise data warehousing, master data management, data quality, data governance, Big Data, business intelligence, visualization.
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Enterprise modeling tool and data integration platform (Erwin, Embarcadero, Informatica, Talend, SSIS, DataStage, Pentaho) Hands-on experience in two (or more) areas of the data and analytics technical domains - Enterprise cloud data warehousing, integration, preparation, and visualization along with artificial intelligence, machine learning, data science, data modeling, data management, and data governance.
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Work with big data technologies like Azure Databricks, Apache Spark & Palantir. Experience with Data integration, data processing, data streaming, message queuing techniques.
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An intimate understanding of data processing, data integration, real-time analytics, data and analytic product development, data security, data governance, metadata management.
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Big Data, Cloud enablement, TOGAF, Database architecture principles, Mainframe - COBOL, DB2. Experience with Big Data technologies like Apache Hadoop, HBase, Spark, Hive. Experience with designing Scalable High Resiliency Systems, Business Process Management, Camuda BPM is preferred, complex system integration projects.
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Big Data Tools: Kubernetes (K8s), Hadoop, Spark, Postgres, Databricks, Azure cloud offerings including (Azure Synapse, ADLS, Azure Functions) Stay up-to-date with emerging trends and advancements in data engineering, and champion their integration into our processes and technologies (especially as it relates to HR technology.
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Work directly with Product owners and AMEX developers and leaderships team Expertise in: Google Cloud Platform, Cloud Composer, Big Query, Lead Migration Participate in requirement gathering and analysis Comfortable in Agile Able to be onsite 3-5 days per week Lead the effort in turning data into actionable business insights Strong Py Spark developer with hands on experience.
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Experience with Big Data tools - Azure Synapse Analytics, Azure Databricks, Azure HDInsight, Azure Data Lake Storage, Azure Cosmos DB or its equivalent in AWS/GCP/OpenSource (Apache tools: Hadoop, Spark, Kafka, HBase, Cassandra, NiFi, Storm etc.
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Design business intelligence reports, interactive dashboards and KPI scorecards using Power BI. Use data architecture, data management, data integration, Snowflake Data Cloud, Azure, AWS, DBT, Airflow, Spark, Snowpark, and Python.
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Presto, Hive, SparkSQL, Cassandra, or Solr other Big Data query and transformation experience a plus. Expertise in Cloud Data Warehouses in Redshift, BigQuery or analogous architectures a plus.
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Data warehousing platforms (e.g., Amazon Redshift, Snowflake) and big data technologies (e.g., Hadoop, Spark). The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production generative AI systems.
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