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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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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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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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Data Governance: Establish and enforce data governance policies and procedures to ensure data quality, security, and compliance with regulatory requirements. Performance Optimization: Optimize data systems performance through indexing, partitioning, and other techniques, ensuring scalability and responsiveness for analytical and reporting needs.
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Data Systems Architecture: Design and implement scalable and reliable data systems architecture to support the organization's data needs, including data storage, processing, and analytics. Data Storage Solutions: Architect and implement data storage solutions, including relational databases, NoSQL databases, data warehouses, and data lakes, ensuring optimal performance, reliability, and scalability.
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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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Data Integration: Integrate data from diverse sources and systems into data systems infrastructure, ensuring data consistency, integrity, and security. Strong understanding of data integration concepts and techniques, with experience integrating data from diverse sources and systems.
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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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Data Processing Frameworks: Lead the implementation and optimization of 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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8+ years of experience in data engineering or systems engineering, with a focus on data systems technologies. Flexible work arrangements, including remote work options and flexible hours. Performance Optimization: Lead efforts to optimize data systems performance through indexing, partitioning, and other techniques, ensuring scalability and responsiveness for analytical and reporting needs.
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Implement and maintain data models and structures in databases like Oracle, PostgreSQL, MySQL, Redshift, Snowflake, and more. Advanced skills in SQL, T-SQL, and experience with various databases (e.g., Oracle, MySQL, PostgreSQL.
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Flexible work hours and remote work options. Apply now to join our team and be part of the data revolution! State-of-the-art technology environment with access to cutting-edge tools and resources. You'll work on challenging projects, from architecting data storage solutions to implementing data processing frameworks, to support the needs of our data-driven organization.
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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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Programming in Python or Ruby, utilizing AWS S3, MongoDB, PostgreSQL, AWS Redshift or similar database technologies; Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.
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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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