Lead - Data Engineering ( Palantir)

2 months ago

Kanpur, India Lear Corporation Full time

We’re Lear For You


Lear, a global automotive technology leader in Seating and E-Systems, is Making every drive better by delivering intelligent in-vehicle experiences for customers around the world. With over 100 years of experience, Lear has earned a legacy of operational excellence while building its future on innovation. Our talented team is committed to creating products that ensure the comfort, well-being, convenience, and safety of consumers. Working together, we are Making every drive better™.


To know more about Lear please visit our career site: www.lear.com


We're Hiring: Lead- Data Engineering (Palantir Foundry) at Lear Corporation


Are you a data engineering expert ready to drive impact and support crucial decision-making? Join Lear Corporation as a **Senior Data Engineer** to take the lead on designing, building, and maintaining scalable data pipelines within Palantir Foundry.


About the Role:

In this role, you’ll collaborate with cross-functional teams to ensure data solutions are robust, reliable, and aligned with our organizational needs. Your expertise will shape the future of data-driven insights and support mission-critical projects that drive decision-making. You’ll also mentor junior team members, helping foster a collaborative and innovative environment.


✨ Key Responsibilities:

1. Manage Execution of Data-Focused Projects:

  • As a senior member of the LEAR foundry team, support in designing, building and maintaining data-focused projects using Lear’s data analytics and application platforms.
  • Participate in projects from conception to root cause analytics and solution deployment.
  • Understand program and product delivery phases, contributing expert analysis across the lifecycle. Ensure Project deliverables are met as per agreed timeline.

2. Tools and Technologies:

  • Utilize key tools within Palantir Foundry, including:

 Pipeline Builder: Author data pipelines using a visual interface.

 Code Repositories: Manage code for data pipeline development.

 Data Lineage: Visualize end-to-end data flows.

  • Leverage programmatic health checks to ensure pipeline durability.
  • Work with both new and legacy technologies to integrate separate data feeds and transform them into new scalable datasets.
  • Mentor junior data engineers on best practices.

3. Data Pipeline Architecture and Development:

  • Lead the design and implementation of complex data pipelines.
  • Collaborate with cross-functional teams to ensure scalability, reliability, and efficiency and utilize Git concepts for version control and collaborative development.
  • Optimize data ingestion, transformation, and enrichment processes.

4. Big Data, Dataset Creation and Maintenance:

  • Utilize pipeline or code repository to transform big data into manageable datasets and produce high-quality datasets that meet the organization’s needs.
  • Implement optimum build time to ensure effective utilization of resource.

5. High-Quality Dataset Production:

  • Produce and maintain datasets that meet organizational needs.
  • Optimize the size and build scheduled of datasets to reflect the latest information.
  • Implement data quality health checks and validation.

6. Collaboration and Leadership:

  • Work closely with data scientists, analysts, and operational teams.
  • Provide technical guidance and foster a collaborative environment.
  • Champion transparency and effective decision-making.

7. Continuous Improvement:

  • Stay abreast of industry trends and emerging technologies.
  • Enhance pipeline performance, reliability, and maintainability.
  • Contribute to the evolution of Foundry’s data engineering capabilities.

8. Compliance and data security:

  • Ensure documentation and procedures align with internal practices (ITPM) and Sarbanes Oxley requirements, continuously improving them.

9. Team Development and Collaboration:

  • Mentor junior team members and contribute to their growth.
  • Foster collaboration within cross-functional teams.
  • Share best practices and encourage knowledge sharing.

10. Quality Assurance & Optimization:

  • Optimize data pipelines and their impact on resource utilization of downstream processes.
  • Continuously test and improve data pipeline performance and reliability.
  • Optimize system performance for all deployed resources.


Qualifications:

  • Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
  • Minimum 5 years of experience in data engineering, ETL, and data integration.
  • Proficiency in Python and libraries like Pyspark, Pandas, Numpy.
  • Strong understanding of Palantir Foundry and its capabilities.
  • Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka).
  • Excellent problem-solving skills and attention to detail.
  • Effective communication and leadership abilities.


**Why Lear?**

Be part of a team that values innovation, excellence, and growth. Join us and make a real impact in a role that blends hands-on technical expertise with leadership in a collaborative, cutting-edge environment.


Ready to advance your data engineering career with Lear? **Apply now**

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