Senior Data Engineer

As a Senior Data Engineer at our company, your key responsibilities include developing scalable data models in Palantir Foundry, automating data ingestion processes, and collaborating with analysts to create insightful Power BI dashboards. You'll also work on normalizing data from ERP systems to ensure consistent insights across various clients.

To qualify for the Senior Data Engineer position at our company, candidates should have 7-10 years of experience in data engineering, strong software engineering foundations, and expertise in Palantir Foundry. Additionally, proficiency in SQL, experience with ETL pipeline creation, and knowledge of ERP data systems such as SAP and JD Edwards are essential.

In your role as a Senior Data Engineer at our company, you'll closely collaborate with the Product Manager and data analysts to align on normalization rules and data requirements, ensuring that solutions are modular and reusable. This teamwork is vital for delivering high-quality data insights effectively.

Yes, experience with Power BI is necessary for the Senior Data Engineer role at our company. You'll be responsible for creating dashboards and integrating data models into Power BI to provide stakeholders with clear visual insights derived from clean and normalized data.

As a Senior Data Engineer at our company, you will primarily use Palantir Foundry for data modeling and pipeline construction. Additionally, you will work with Power BI for visualization and data presentation, as well as tools for integrating external datasets like Cap IQ into our workflows.

When answering this question, highlight specific projects where you utilized Palantir Foundry for data modeling and pipeline development. Mention any challenges faced and how you overcame them, emphasizing your skills in building scalable data-oriented solutions.

Discuss your methodology for normalizing data from various sources, such as ERP systems. Give examples of how you’ve ensured cross-client consistency and how you've handled common data quality issues during the normalization process.

Describe the techniques you've employed to optimize ETL pipelines, such as automating data ingestion or using efficient data transformation processes. Highlight a specific instance where your optimization led to significant improvements in performance or reliability.

To ensure data accuracy, talk about your techniques for data validation and cleansing. You might mention using automated checks as well as manual reviews when necessary, along with your experience in maintaining data integrity while transitioning between systems.

Share examples of how you've leveraged SQL for data querying and transformation. Specify the types of complex queries you've written or performance optimizations you've made to enhance data retrieval in your previous roles.

When discussing collaboration with data analysts, provide examples of successful partnerships on projects, how you streamlined communication, and any impactful outcomes that resulted from this teamwork, especially in developing impactful data visualizations.

Give a specific example of a project where you integrated external datasets, detailing the data sources used, challenges faced, and how this integration added value to your data analysis efforts.

Express your preference for tools like Power BI for data visualization and explain why you prefer them in terms of usability, integration capabilities, and how they support effective data storytelling.

Mention various resources such as industry blogs, forums, courses, or events you partake in to stay updated on data engineering trends. Demonstrating a commitment to continuous learning is crucial in this fast-evolving field.

Share a specific challenge you faced in a past data engineering project and explain the steps you took to resolve it, putting emphasis on your problem-solving skills and the impact of your solution on the project.