UCLA Health DGIT Data Operations is looking for a Senior Data Engineer to join the team. The candidate should have hands on experience working with Cloud data platforms such as AWS or Azure, have created data pipelines using services such as Glue or Synapse/Data Factory. In addition, experience in building data pipelines using Python and automating task deployment via Terraform would be a plus. To support data operations of David Geffen School of Medicine Dean’s Office, this position requires extensive knowledge in data engineering, requires the use of modern AWS or Microsoft Azure Services including but not limited to AWS Glue, AWS Kinesis, AWS S3, AWS Redshift, AWS RDS, AWS Snowflake, Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Data Lake Storage, Azure SQL Database, AWS/Azure Databricks, and Azure Cosmos DB to build pipelines and manage dataflows to ensure data operation efficiency. This position will be responsible for designing and implementing data pipelines, integrating data from various sources, and ensuring data security and privacy. They should also have experience in data processing and management and be able to monitor and optimize data pipeline performance, fimiliarity with Apache Airflow is a plus. The ideal candidate will have a passion for high perfoemance cloud computing and data storage solutions, with expertise in Amazon AWS and/or Microsoft Azure. They should also be familiar with the latest technologies and trends in data engineering and cloud computing, with the ability to stay up-to-date and continue to drive innovation in the field. Additionally, the ideal candidate should have strong communication and collaboration skills, as they will be working closely with other members of the data and technology teams. They should have a problem-solving and analytical mindset, with the ability to effectively troubleshoot and optimize data pipeline performance.

Candidates should have a Bachelor of Science degree, Masters Preferred; degree should be in computer science, electric engineering, science, education, research; with a minimum of 5 years’ experience in academic, finance, or healthcare industry preferred. Strong knowledge of cloud computing, experience with Amazon AWS and Microsoft Azure. Experience with data ingestion and processing from various sources such as REST APIs, cloud-based data stores, and on-premises databases Ability to design and implement data pipelines for batch and real-time data processing. Experience with Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Databricks, and other Azure data processing and storage solutions Strong knowledge of SQL and NoSQL databases, including Azure SQL Database, Azure Cosmos DB, and Azure Data Lake Storage. (or AWS equivalents) Strong knowledge relational and dimensional data modeling, and data cube designs such as multi-dimensional vs. columnar. Detailed working knowledge of programming and scripting languages such as PL/SQL, T-SQL, Python, .NET, JavaScript or Java, C#. Knowledge of big data technologies such as Hadoop, Spark, and Hive. Familiar with MapReduce framework. Experience with NoSQL database systems (HBase, Cassandra, MongoDB etc.) and distributed file system. Knowledge of data security and privacy regulations (e.g. FERPA). Experience working with data from the following sectors: healthcare, education, and financial. Excellent communication skill is required for this position with the ability to manage multiple requests. This position must be able to provide expert level recommendation on visualizations and best practices support distributed BI developers outside of unit. Ability to keep up to date with the latest trends and technologies in the field of data engineering and cloud computing.