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4 years of experience in working with high-scale or distributed RDBMS and/or NoSQL platforms (e.g. PostgreSQL, Redshift, BigQuery, Couchbase, MongoDB, Cassandra, HBase, Neo4J etc. 4 years of experience in working with high-scale or distributed RDBMS and/or NoSQL platforms (e.g. PostgreSQL, Redshift, BigQuery, Couchbase, MongoDB, Cassandra, HBase, Neo4J etc.
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This position will be responsible for the implementation, maintenance, development, and performance of critical databases with primary focus on Microsoft SQL Server, Azure, Amazon Redshift, MySQL, PostgreSQL, and Amazon RDS. The qualified candidate will display excellent problem solving, analytical, communication, coordination and task management skills.
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Understanding of database platforms (On-Prem & Cloud-based): SQL Server, MySQL, AWS Redshift, Google PostgreSQL, Oracle. Experience with healthcare terminology concept codes (SNOMED, ICD-10, LOINC, etc.
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Experience with Salesforce, AWS Redshift, and Snowflake. Proficiency in SQL and experience with database management systems (e.g., MySQL, PostgreSQL, MongoDB). Experience with Salesforce, AWS Redshift, and Snowflake.
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Experience working with Cloud Data Warehouses/Platforms, especially Snowflake, Redshift, Databricks, and BigQuery. Expertise in one or more of the following RDBMSes: PostgreSQL, MySQL, Oracle, SQL Server.
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Hands-on development experience with Relational DBs (MS SQL Server, PostgreSQL, Oracle), as well as complex stored procedures and functions using SQL and optimization of SSIS packages and Redshift database queries.
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Proficiency in data storage technologies such as relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), data warehouses (e.g., Snowflake, Redshift), and data lakes (e.g., Amazon S3, Azure Data Lake Storage.
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MongoDB, JSON, SQL, XMLHigh experience level with back-end programming languages (Java/Spring, Python)Working knowledge with datastores MongoDB, DynamoDB, PostgreSQL, S3, Redshift, Redis, and ElasticsearchFamiliar with technologies jQuery, ASP.
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Programming in Python or Ruby, utilizing AWS S3, MongoDB, PostgreSQL, AWS Redshift or similar database technologies; experience in using Jupyter notebooks and one or more statistical visualization or graphing toolkits such as Excel, Qlik Sense or Tableau.
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Expertise in data storage technologies such as relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), data warehouses (e.g., Snowflake, Redshift), and data lakes (e.g., Amazon S3, Azure Data Lake Storage.
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Strong programming skills in languages such as Python, Java, or Scala, with experience in data processing frameworks like Apache Spark or Apache Flink. Data Processing Frameworks: Implement and optimize data processing frameworks and technologies, such as Apache Hadoop, Apache Spark, and Apache Flink, to enable efficient data processing and analysis.
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As a Senior Data Engineer on the MMA National IT Data and Analytics Team, we will count on you to develop and maintain scalable data pipelines, enhance data models, and provide subject matter expertise in data acquisition and consumption pipelines on Azure, Databricks and Confluent.
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PostgreSQL , MySQL, Aurora, RDS/ Document DB/ Redshift, etc. Proficiency in AWS services, including Kinesis, Kafka, S3, EC2, Lambda, and AWS data stores like Redshift and. PostgreSQL , MySQL, Aurora, RDS/ Document DB/ Redshift, etc.
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Experience with AWS ecosystem (EMR, EC2, S3, RDS, Redshift, Aurora, Athena, RDS, PostgreSQL). Experience with AWS ecosystem (EMR, EC2, S3, RDS, Redshift, Aurora, Athena, RDS, PostgreSQL.
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Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and services like AWS Glue, Azure Data Factory, or Google Dataflow. 5+ years of experience in data engineering or systems engineering, with a focus on data systems technologies.
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