Senior Data Engineer (Palantir)

As a Senior Data Engineer at Nagarro, your primary responsibilities include developing technical plans for data migration, ensuring data quality throughout the pipeline, creating scalable frameworks for data ingestion, and providing technical support to your team. You will also work with event-based technologies and stay updated with trends in cloud computing and data analytics.

To qualify for the Senior Data Engineer role at Nagarro, candidates should have 8 to 10 years of IT experience, including a minimum of 4 years specifically working with Palantir. Furthermore, experience with cloud technologies such as Azure or Google, data modeling, and strong SQL skills are essential.

A Senior Data Engineer at Nagarro contributes to the BAI team by developing effective data solutions that support various business functions. This includes aligning technical strategies with business needs and ensuring high-quality data management practices that enhance analytics and reporting capabilities.

In the Senior Data Engineer position at Nagarro, familiarity with Azure's Data Factory, data lakes, PySpark, and SQL is vital. Additionally, experience with event-based technologies and data visualization tools will be highly beneficial in executing tasks effectively.

For a Senior Data Engineer at Nagarro, strong communication skills are crucial for conveying technical concepts to diverse stakeholders. Collaboration, problem-solving abilities, and the capability to manage multiple projects simultaneously are also important soft skills for success in this role.

When discussing your Palantir experience, elaborate on specific projects where you utilized its features to solve complex data problems. Highlight any innovations you've introduced or efficiencies you've created, and how they impacted your team's output.

To ensure data quality, discuss methodologies you've employed for data validation and cleansing throughout the pipeline. Provide examples of how these strategies enhanced the reliability of the insights your team was able to draw from the data.

Outline a specific challenge you encountered during a migration process, detailing the steps you took to overcome it. Discuss the outcome and any lessons learned that you can apply to future projects.

Share experiences where you've effectively leveraged Azure services, documenting how you've implemented data processing and storage solutions to optimize workflows in your previous roles.

Discuss strategies for staying updated, such as attending workshops, following industry leaders, or working on personal projects. Convey your enthusiasm for continuous learning and how it translates to your role as a Data Engineer.

Explain that data modeling is crucial for structuring data outputs and understanding complex relationships between data sets. Provide examples of how effective data modeling has led to high-impact business insights in your past work.

Detail your experience in creating and implementing data ingestion frameworks. Highlight specific tools and techniques you've employed and the improvements made to data processing efficiency as a result.

Discuss your approach to resolving disagreements, emphasizing the importance of collaboration and open dialogue. Mention how you would present your ideas while also considering the perspectives and expertise of your colleagues.

Outline a strategy for preparing data for machine learning, highlighting tasks like data cleaning, transformation, and feature selection. Discuss any tools you've used and the importance of having quality data for ML accuracy.

Share your methodology for prioritizing tasks based on urgency and impact. Emphasize your adaptability and time management skills to convey your ability to handle multiple projects efficiently.