Lead Data Engineer

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents are valued and celebrated. At JPMorganChase, you'll help create a brighter future and make a meaningful difference through impactful data engineering. You'll work with agile teams to deliver trusted solutions that power our business. We foster a culture of inclusion, opportunity, and respect. Your contributions will help shape the future of technology.
As a Lead Data Engineer in the Compute Infrastructure Platforms team, you will design, build, and maintain critical data pipelines and architectures. You will collaborate with agile teams to deliver secure, stable, and scalable data solutions that support the firm's business objectives. Your expertise will ensure high-quality data collection, storage, access, and analytics. You will help drive innovation and operational excellence. Together, we'll create solutions that make a difference.
Job Responsibilities
Design and deliver scalable, secure, and reliable data pipelines using Python and Databricks to support enterprise-level business needs.
Develop and maintain data models and architectures that enable high-quality analytics and reporting across multiple business functions.
Drive root cause analysis and corrective action for data quality issues, ensuring consumers can trust the data they rely on.
Implement and manage database backup, recovery, and archiving strategies to ensure data availability and resilience.
Evaluate and report on access control processes to determine the effectiveness of data asset security with minimal supervision.
Collaborate with cross-functional teams to define data requirements and deliver solutions aligned with business objectives.
Identify opportunities to optimize data workflows and improve overall pipeline performance and efficiency.
Contribute to a team culture of diversity, opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills
Formal training or certification on data engineering concepts and advanced applied experience.
Hands-on experience developing and maintaining data pipelines using Python.
Proficiency with Databricks for large-scale data processing and analytics.
Experience working with both relational and NoSQL databases.
Proficiency across the full data lifecycle, including ingestion, transformation, storage, and access.
Experience implementing database backup, recovery, and archiving strategies.
Strong understanding of data quality principles and experience driving root cause analysis for data issues.
Preferred qualifications, capabilities, and skills
Experience working with enterprise-level datasets in a large, complex organization.
Familiarity with cloud-based data platforms and modern data architecture patterns.
Experience in site reliability engineering or infrastructure deployment roles.
Knowledge of data governance, access control, and security best practices.