Data Engineer

Mid / Senior



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About This Workplace

Meytier Partner

Join our diverse and inclusive team where you will feel valued and motivated to contribute with your unique skills and experience. Exavalu offers permanent remote working model as we believe in going where the right talent is.

Key Responsibilities:

  • Develop, implement, support, and operationalize AWS data lake infrastructure & services.
  • Create and maintain optimal data pipeline architecture,
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, ETL (e.g., Informatica Cloud) and AWS ‘Data Lake’ technologies.
  • Build analytics tools that utilize the data pipeline to provide actionable insights into patient care, operational efficiency, and other key business performance metrics.
  • Develop a deep understanding of AWS’s vast data sources and know exactly how, when, and which data to use to solve business problems.
  • Monitor and maintain data lake security and data lake services.
  • Manage numerous requests concurrently and strategically, prioritizing when necessary.
  • Troubleshoot technical issues and provide solutions and fixes using various tools and information such as server logs and report debug logs.
  • General and administrative tasks

Desired Profile:

  • Bachelor’s degree in computer science, Information Systems, Mathematics, or a related discipline.
  • 4+ years of experience in Information Technology within a complex, matrixed, and global business environment.
  • Experienced as a data engineer with AWS Data Lake Technologies and Services
  • Expert working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
  • Building and optimizing AWS ‘Data Lake’ data pipelines, architectures, and data sets.
  • Strong analytic skills related to working with unstructured datasets.
  • Build processes supporting data transformation, data structures, metadata, dependency, and workload management.
  • A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
  • Understanding of message queuing, stream processing, and highly scalable AWS ‘Data Lake’ data stores.
  • Understanding of database and analytical technologies in the industry including MPP and NoSQL databases (e.g., Snowflake), Data Warehouse design, ETL, BI reporting and Dashboard development.
  • Experience with Agile framework and DevOps.
  • 3+yrs of experience in building ETL data pipelines using AWS Glue and Pyspark.
  • Efficient in developing Spark scripts for data ingestion, aggregation, and transformation.
  • Exception Handling and performance optimization techniques on python/pyspark scripts

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