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Experience with Quantum Metrics and Akamai, Technologies and languages: Python, Spark, R, etc. Experience with Palantir Foundry, Microsoft SQL Server Management Studio. Experience analyzing ecommerce platforms for Web, Mobile App, Loyalty, Demonstrated experience in handling massive data sets and relational databases (SQL and Non-SQL.
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL, Confluence, JIRAAmazon Web Services (Security Groups, Batch, Lambda, S3, ECS, EC2, ALB, RDS, IAM, SNS, Fargate, ElastiCache, Redis.
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Development experience (SQL, Python, R, Java, C#, Spring, REST/SOAP, Hibernate, etc.) Experience in data architecture, data warehousing including non-relational and relational databases (e.g., SQL, MySQL, Hadoop, MongoDB), big data (Hadoop, MapReduce, Cloudera, HDFS, Hive, PIG, Impala.
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Our platform is ever evolving, but currently is a combination of SQL, Kafka, Flume, Spark, Scala, Java, Python, NoSQL (HBase, Cassandra and ScyllaDB), MPP RDBMS, Postgres, Hadoop, AWS, AirFlow, Docker, Kubernetes, and Elastic.
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Any level of experience with Utilize technical skills including Spark, Python, Java, Scala, SQL, RESTful APIs and Cloud Services including EMR, Keyspaces, Elasticache, Terraform, Data Lake formation, Redshift and data streaming technologies including Apache Kafka, Kinesis to develop and manage multiple assignments, including large and critical data pipeline projects to assure timely and cost-effective deliveries.
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Knowledge of Data Warehouse technology (Unix/Teradata/Ab Initio/Python/Spark/Snowflake/No SQL) Experience in building & utilizing tools and frameworks within the Big Data ecosystem including Kafka, Spark, and NoSQL.
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Understanding of Python, Apache Spark, Apache Hadoop. Python, Apache Spark. Experience with SQL databases, writing efficient queries and data modeling (e.g. Postgresql, AWS Aurora, AWS Redshift.
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Advanced level SQL skills. Google Data Analytics Professional Certificate, Power BI Data Analyst Associate, AWS Certified Data Analytics, Enterprise Data Warehouse certifications, Palantir, Client Side performance metrics tools e.g.
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Data processing programming using SQL, DBT, Python, and similar tools. Experience in implementing data pipelines for both streaming and batch integrations using tools/frameworks like Azure Data Factory, Glue ETL, Lambda, Spark, Spark Streaming, etc.
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Lead the design, development, and maintenance of multi-modal training content for Databricks customers and partners using the latest versions of Delta Lake, Python, SQL and Spark. Strong proficiency in Python and SQL.
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5+ years of experience in application development including Python, SQL, Scala, or Java. Senior Data Engineer (Python/SQL/Bigdata) Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake.
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL, Confluence, JIRA. Utilize programming languages such as Java, Python, SQL, Node, Go, and Scala, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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Technical Environment: Python and PySpark programming; Hadoop; HDFS, map-reduce, YARN, AWS EMR; Hive, HBase, parquet, ORC, Spark SQL, Sqoop, Apache Hudi; orchestrating ETL pipelines involving data sourcing, transformations and publishing using Apache Airflow; Apache Kafka; Advanced SQL for data profiling and data validation; Unix commands and scripting; JIRA, Gitlab, and Subversion; EMR; S3; RDS; MWAA; Jenkins; CircleCI; Agile; AWS.
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Python coding skills are also required to ingestion, transform and analyze large datasets via complex SQL. Ability to understand complex SQL and highly complex Python code is critical.
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5+ years of hands-on experience in architecting Big Data applications using the Hadoop ecosystem, such as Hadoop (HDFS, YARN, MapReduce), Spark (Spark SQL Spark Streaming, MLLib), Pig, Hive, HBase, Sqoop, Oozie.
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