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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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Experience and understanding of various core AWS services such as IAM, Cloud Formation, EC2, S3, EMR/Spark, Glue, Datasync, CloudHealth, CloudWatch, Lambda, Athena, and Redshift. Solid experience of AWS services such as CloudFormation, S3, Glue, EMR/ Spark, RDS, Redshift, DynamoDB, Lambda, Step Functions, IAM, KMS, SM etc.
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3+ years of experience with Big Data tools/technologies like Hadoop, Spark, Spark SQL, Kafka,Sqoop, Hive, S3, HDFS, or. Experience with Databricks UI, Managing Databricks Notebooks, Delta Lake with Python, DeltaLakewith Spark SQL, Delta Live Tables, Unity Catalog.
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL. Amazon Web Services (Security Groups, Batch, Lambda, SSH, S3, ECS, EC2, ALB, RDS, IAM, SNS, Fargate, ElastiCache, Redis.
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Data indexing, search, and analytics, i.e., Elasticsearch, Spark, Databricks, Delta Lake. AWS GovCloud tools and technologies including AWS RDS, Lambda, OpenSearch (Elasticsearch), ElasticCashe for managed Redis Cache, ALB for load balancing, EC2, S3, Elemental MediaConvert, SSO, EventBridge, MSK for managed Kafka, KMS.
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DevOps pipeline tools including Ansible, Jenkins, Terraform, Git, Nexus, and Hashicorp Vault for deployment pipeline. Terraform and Ansible for Infrastructure as Code based provisioning & configuration management.
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You have hands-on experience in Java/Scala/Python, Spark, S3, Glue, Redshift. Must have skill set: Java, Scala , Python, Spark, S3, Glue, Redshift. Experience with Spark scala and java programming.
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3+ years of experience with 1+ Cloud Data Warehousing technologies: Redshift, BigQuery, Spark, Snowflake, Presto, Athena, S3. Familiarity with distributed processing: Dask and/or Spark.
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SQL, Python, PySpark , S3, Lambda, EMR, Glue, Athena, EC2, IAM, Redshift, DMS, Airflow, Jenkins, Snowflake. We are looking for a strong Data Engineer cum Architect, to create new modern data ingestion pipelines using latest technologies like AWS Athena, Lambdas, Python, Spark.
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Should have a very good hands-on experience in Cloud DB platforms (Snowflake is preferable), Building data pipelines & SQL, Python for Data Engineering. Manage and perform data operations and data engineering requirements including automation and optimization.
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2+ years of experience with Spark. 2+ years of experience with Presto or Snowflake. At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI.
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Hands-on experience using multiple data platforms and tools (e.g. Airflow, Hive, Kafka, Postgres, Redshift, S3, Spark, Trino) We use Apache Airflow, Python, and Apache Spark for ETL. For streaming data, we use Apache Kafka managed by a vendor and use Kafka Connect, Kafka Streams, and Spark Streaming for stream processing.
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O AWS - Elastic Map Reduce, Athena, Redshift, Kinesis, Glue, S3. Machine Learning experience with either Azure ML, Spark Mllib, TensorFlow, or similar. Continue to evolve and improve technical skills with SQL, Python, Spark, and other emerging data management technologies.
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3+ years of experience scaling and operating distributed systems like big data processing engines (e.g., Apache Hadoop, Apache Spark), distributed file systems (e.g. HDFS, CEPH, S3, etc.
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Therefore, we prioritize individuals coming from the following communities: Refugee, Neurodivergent, Single Parent, Blind or Low Vision, Deaf or Hard of Hearing, Black, Hispanic, Asian, Military Veterans, the Elderly, the LGBTQ, and Justice Impacted individuals.
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