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Job DescriptionJob DescriptionLocations:
Merritts Island, FL
Seattle, WA
Huntsville, AL

1. Describe a challenging data pipeline issue you encountered in the past and how you resolved it? Please list the steps you took and how managed to find root cause.

2. What ETL tools and data management platform have you worked with? Can you provide examples of how you used these tools to optimize data workflows and improve data quality.

3. Share an example of a big cross-functional project you were part of that was related to a data related objective? What was your role and how did you help meet the objective.


Responsibilities for the Data Engineer:

  • Federate Data Ingestion and Curation: Lead the efforts to streamline data ingestion and curation processes, ensuring efficient and scalable data handling within the Palantir Foundry platform.
  • Hydrate Ontology: Spearhead the development and maintenance of the ontology, facilitating a structured and intuitive understanding of the data landscape.
  • Define Data Pipeline Standards: Establish common standards for data pipelines, focusing on data quality, RBAC, and data classification to maintain integrity and security.
  • Develop Data Patterns and Standards: Create and implement patterns and standards for CDC, incremental loads, and real-time data streaming, enhancing the platform's responsiveness and flexibility.
  • Lead Ontology Build: Guide the construction of the ontology, ensuring it meets the complex needs of aerospace manufacturing data.
  • Mentor Data Engineers: Provide leadership and mentorship to other data engineers, promoting the adoption of standards, patterns, and DataOps practices for federating data pipeline work across the enterprise.
  • Accelerate Delivery and Self-Service: Drive the acceleration of data pipeline delivery and promote data self-service consumption, enabling faster decision-making and innovation.
  • DevOps Practices: Design and implement DevOps practices for data operations, ensuring continuous integration, continuous delivery, and automated testing are at the core of the data engineering workflow.
  • Monitoring and Observability: Establish comprehensive monitoring and observability frameworks for the data platform, enabling proactive issue detection and resolution to maintain high service availability.
  • Technology Stack Experience:Programming Languages - Python, PySpark, Java, SQL
  • Databases - Postgres, Oracle
  • Cloud Services AWS, Azure, GCP (storage, compute, databases, catalogs, streaming, replication, Queueing & Notification, Logging & Monitoring service) any one of the cloud platforms is good but AWS is preferred.
  • Big Data Frameworks
  • Metadata Catalogs DataHub, Informatica, Collibra (experience in any one is good)
  • Data Quality Platforms Anomolo, Informatica, BigEye (experience in any one is good)
  • Event Platforms Kafka, MSK
  • Data Platforms Palantir Foundry, Databricks, Snowflake (experience in any one is good but Foundry experience will be a bonus)

Qualifications for the Data Engineer:

  • Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.
  • 5-8 years required in the data engineering & data management with a strong focus on data platforms like Databricks, Snowflake or Palantir Foundry
  • Extensive knowledge of data ingestion, ETL processes, and data modeling.
  • Strong problem-solving skills and the ability to work with complex data integration challenges.
  • Experience with data reconciliation, data pipeline & data monitoring, and performance optimization.
  • Excellent communication and teamwork skills
  • Leadership experience and the ability to mentor junior engineers.
  • Prior experience in aerospace manufacturing or a related industry is a plus
  • Ability to earn trust, maintain positive and professional relationships, and contribute to a culture of inclusion