Design and develop business-critical backend systems using stream processors and high-quality data pipelines.
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and Azure big data technologies.
- Build a cloud-native, real-time stream processing data lake platform that scales into the Zettabytes and beyond.
- Build a data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
- Ability to automate data pipelines using DevOps tools.
- Perform root cause analysis on external and internal processes and data to identify opportunities for improvement.
- Build processes that support data transformation, workload management, data structures, dependency, and metadata.
- Adopt for change, always open to replacing what you built yesterday with something better today.
- Work with all stakeholders to design and code large-scale batch and real-time data pipelines on Azure.
- Perform code reviews, and lead by example on code refactoring for readability, extensibility, and testability.
Requirements
- A bachelors degree in computer science or equivalent.
- 5+ years of experience as a Data Engineer.
- Experience with Azure big data tools: Azure Databricks, Azure Synapse, HDInsight, ADLS.
- Experience with relational SQL and NoSQL databases.
- Excellent problem solving and analytic skills associated with working on structured and unstructured datasets using Azure big data tools.
- Experience with data pipeline and workflow management tools: ADF and Logic Apps.
- Experience with Azure cloud services: VM, Azure Databricks, Azure SQL DB, Azure Synapse.
- Experience with stream-processing systems: Azure Streams.
- Experience with scripting languages Python, and have strong knowledge in developing Python notebooks.
