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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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Knowledge of data warehousing architectures, data integration techniques, and ETL tools (e.g., Informatica, Talend, Apache NiFi). Implement data integration and data modeling solutions to ensure the consistency, accuracy, and integrity of healthcare data across different systems and platforms.
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Strong understanding of data modeling, ETL/ELT processes, data integration techniques, and data warehousing concepts. Azure, ADF, Databricks, Data Factory, ADLS KeyVault, Terraform, Azure DevOps, DBT.
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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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Understanding of information management principles and practices (i.e. business intelligence, data warehousing, data architecture, data integration, data quality, data governance, data security, master data and meta data.
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Firm understanding of data modeling, master data management, enterprise data warehousing, ETL, reporting, querying, requirements analysis, and data integration techniques.
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Experience with a breadth of Data Integration tools, Databases, Big Data Platforms, Cloud-based data platforms and Data Streaming technologies, Cloud Azure and GCP platforms.
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In-depth Knowledge of Business Intelligence, Data Warehousing and ETL integration concepts and proven experience (e.g., Star schema vs. Rich experience in data integration, data architecture, data engineering, software engineering / DevOps, data operations, analytics, or BI.
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Strong proficiency in data engineering technologies, such as SQL, Python, and data integration tools. Relevant experience of a minimum of 6 years in data engineering, including data warehousing, ETL development, and data modeling.
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Breadth of technical experience and knowledge, with depth in Data Engineering (data exchange, data processing, and data delivery), Data Warehousing, Business Intelligence, and two or more of the following areas: Data Modeling, Meta Data Management, Master Data Management, AWS Solution Architecture.
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Familiarity with cloud-based Data Warehousing solutions, particularly Snowflake Previous exposure to Fivetran or similar data integration tools. Role: Lead Data Integration Engineer- Snowflake/DBT.
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Firm understanding of data modeling, master data management, data quality, enterprise data warehousing, ETL, reporting, querying, requirements analysis, and data integration techniques and associated tools.
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Programming languages: Java, Pyspark, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. Expertise in ETL/ELT, SQL, and cloud data integration tools (Azure Data Factory, AWS Glue, Informatica, etc.
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Extensive knowledge of data engineering, data integration, and data management concepts, such as APIs, ETL (Extract, Transform, Load), MDM (Master Data Management), CRUD (Create, Read, Update, Delete), Pub-Sub (Publish-Subscribe.
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Experience with data integration tools (e.g., Apache NiFi, Talend, Informatica) and workflow management systems (e.g., Apache Airflow, Luigi). Strong understanding of database technologies (e.g., SQL, NoSQL, BigQuery), data warehousing concepts, and cloud platforms (e.g., AWS, Azure, GCP.
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