Data Engineer

As a Data Engineer at Emids, your primary responsibilities will include developing and managing data pipelines using Python and Databricks, ensuring efficient ETL processes, and collaborating with teams to integrate data from various databases and streaming sources. You will also optimize large-scale data infrastructures and lead data engineering projects that enhance our healthcare solutions.

To succeed as a Data Engineer at Emids, candidates should have at least 6 years of experience in data engineering, a strong command of Python, and expertise in Databricks and Azure services, including Azure SQL Database and Azure Data Factory. Familiarity with data modeling, ETL processes, and Unix shell scripting is also essential.

Data Engineers at Emids utilize a variety of cutting-edge technologies including Python, Apache Spark, and Databricks for data transformations. You will also engage with Azure services like Azure Data Lake and Azure Synapse Analytics, enabling seamless integration and management of vast amounts of healthcare data.

Absolutely! At Emids, we emphasize building a diverse and inclusive culture. As a Data Engineer, you’ll be part of an environment that values unique perspectives and encourages everyone to bring their authentic selves to work, reflecting the diversity of the communities we serve in healthcare.

Emids fosters a collaborative and growth-oriented environment. As a Data Engineer, you will have numerous opportunities for professional development through training sessions, participation in innovative projects, and the chance to work alongside industry experts, allowing you to advance your career in the data engineering field.

In handling data quality issues, focus on implementing validation checks during the ETL process to catch discrepancies early. Describe your approach to identifying data anomalies, applying transformations to clean the data, and conducting regular data audits to maintain high-quality datasets.

When using Azure Data Factory for data integration, first outline how you create pipelines that connect to various data sources. Discuss the process of pulling data from these sources, transforming it as needed, and loading it into target systems, highlighting your understanding of the service's features like triggers, data flows, and monitoring.

Share specific strategies like reducing data load times through parallel processing, leveraging data partitioning, and optimizing SQL queries. Discuss your experience in monitoring ETL performance and how you have adjusted your approach based on analytics results to enhance efficiency.

For a comprehensive answer, describe a relevant project in detail, focusing on your leadership role, the complexity of the data requirements, and team collaboration. Highlight challenges like data source inconsistencies or performance bottlenecks and how you successfully navigated these obstacles.

Detail your experience with PySpark, focusing on specific use cases where you employed it for data transformations, aggregations, or machine learning tasks. Discuss its advantages for large datasets, emphasizing how it fits into your data processing pipeline.

Explain your approach to data modeling, emphasizing techniques such as entity-relationship modeling, normalization, and dimensional modeling. Discuss how these techniques help in structuring data for efficient querying and reporting, particularly in healthcare contexts.

Convey your proactive learning approach by discussing how you keep abreast of industry trends, utilize online resources, attend workshops, and engage with communities. Provide examples of technologies you successfully adopted and integrated into your workflow.

Discuss your understanding of data lakes, their importance in handling unstructured data, and your experience implementing them. Share practical examples where you managed data ingestion and processing within a data lake environment and how it supported analytics.

Describe the significance of scripting languages, especially Python, in automating data workflows, performing data manipulations, and creating reusable scripts for ETL tasks. Dive into specific instances where you've effectively used scripting to streamline processes.

Emphasize the importance of simplifying complex technical concepts for non-technical stakeholders. Discuss your strategies for active listening, using visual aids, and providing data-driven narratives that make your insights accessible and relatable.