Lead Data Engineer- Data Architecture

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Job Summary:
As a Lead Data Engineer at JPMorgan Chase within the Wholesale Credit Risk team, you will lead the strategic transformation of our data landscape. This role offers you the opportunity to modernize our data infrastructure, enabling advanced analytics and data-promoten decision-making across the organization. You will inspire and guide a team of data engineers to optimize data sourcing strategies and deliver innovative, scalable, and secure data solutions. Join us to promote impactful change and foster a culture of collaboration, integrity, and continuous improvement.

Job Responsibilities

• Architect and implement modern, scalable data solutions across hybrid cloud and on-prem environments, addressing both OLTP and OLAP requirements.
• Set high standards for data engineering practices, including data modeling, ETL/ELT, data quality, governance, and metadata management. Directly contribute to solution design and development.
• Oversee, mentor, and develop data engineering team, ensuring alignment with business objectives and technology strategy.
• Drive initiatives to consolidate, de-duplicate, and migrate legacy Oracle databases and big data platforms to modern, cloud-based data platforms.
• Champion best practices in data engineering, security, risk management, regulatory compliance, and automation.
• Present data strategies, architecture decisions, and project updates to senior stakeholders. Collaborate cross-functionally with software engineering, analytics, and business teams.
• Balance urgent operational needs with long-term strategic goals, prioritizing initiatives for maximum business impact.
• Foster a culture of collaboration, integrity, innovation, and continuous improvement within the data engineering organization

• Formal training or certification on data engineering concepts and 5+ years applied experience
• 10+ years in data engineering, with a proven track record of leading large-scale data architecture and transformation projects in hybrid cloud and on-prem environments.
• Deep expertise in data modeling, database design, big data technologies, cloud data platforms (AWS, Azure, GCP), and modern data tools (e.g., Snowflake, Databricks, Airflow).
• Strong experience architecting and optimizing transactional and analytical data systems.
• Mastery of ETL/ELT pipelines, data migration, data quality frameworks, data governance, and metadata management.
• Proficiency in enterprise-grade languages (e.g., Python) and data modeling & engineering tools.
• Solid background in data security, risk management, and regulatory compliance.
• Demonstrated ability to lead, mentor, and inspire data engineering teams, including cross-functional and global teams.
• Excellent interpersonal, presentation, and stakeholder management skills.

• Action-oriented, decisive, drives results systematically.
• Skilled at assessing risk and making decisions with a holistic, big-picture perspective.
• Demonstrates a can-do attitude and leads by example.
• Detail-oriented, able to distinguish between important and urgent tasks.
• Prioritizes helpfulness, mentorship, and team development.
• Acts with integrity.