Principle Data Warehouse Engineer (Executive Director)

As a vital core member of an agile team, your innovative spirit and unparalleled knowledge is a catalyst for change and serves as an inspiration for bringing innovative solutions to life. Your visionary leadership and pursuit of excellence makes a lasting impact on the firm and industry.

 As a Principal Data Engineer at JPMorganChase within the [insert LOB or sub LOB], you provide expertise and data engineering excellence as an integral part of an agile team to enhance, build, and deliver data collection, storage, access, and analytic solutions in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various data pipelines and data architectures to support one or more of the firm’s portfolios.

Job responsibilities

 

• Proactively partners with the principle architect to transform design intent into a practical delivery roadmap, ensuring short, critical deliverables are met in a scalable, strategic program.
• Designs, builds, and optimizes complex data warehouse solutions, ensuring scalability, reliability, and performance through hands-on engineering and advanced technical expertise
• Develops and implements robust data pipelines and ETL processes, directly contributing to the team’s delivery of secure and stable data architectures
• Applies advanced data modeling techniques, including linear algebra, statistics, and geometrical algorithms to solve real world business challenges and drive actionable insights
• Collaborates closely with cross-functional teams to integrate data solutions into business-critical applications, actively participating in code reviews and architectural decisions
• Evaluates and selects data visualization tools, and creates impactful visualizations to communicate key trends and insights to stakeholders
• Provides technical mentorship to junior engineers, sharing best practices and fostering a culture of continuous learning through hands-on guidance
• Champions the firm’s culture of diversity, opportunity, inclusion, and respect, modeling these values in daily interactions and project work

 

Required qualifications, capabilities, and skills

 

• Minimum 8 years of hands-on experience in data engineering, designing, building, and delivering large-scale data warehouse solutions in enterprise environments
• Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines
• Proven track record of leading end-to-end delivery of data pipelines, ETL processes, and data architecture projects, directly contributing to successful outcomes
• Experience leading a product as a Principle Engineer or Technical Delivery Lead
• Ability to present and effectively communicate technical concepts, project progress, and solution impacts to Senior Leaders and Executives
• Demonstrated experience in the domain of emerging technologies and their best practices, with a focus on practical implementation and integration
• Shows a proficient understanding of existing data management systems and continuous learning to understand new data management systems
• Strong proficiency in hands-on development using industry-standard tools and languages (e.g., SQL, Python, Spark, cloud platforms), with a commitment to code quality and delivery excellence

 Preferred qualifications, capabilities, and skills 

• Hands-on experience with Databricks for scalable data engineering, analytics, and collaborative development, as well as proficiency in managing large datasets using Apache Iceberg for efficient, reliable table management and data lake optimization
• Experience architecting and implementing enterprise-scale data warehouses on cloud platforms (e.g., AWS, Azure, GCP), including migration from legacy systems
• Advanced proficiency in designing and optimizing data models for analytics, reporting, and business intelligence use cases
• Demonstrated ability to automate data pipeline deployment and monitoring using CI/CD tools and infrastructure-as-code practices
• Hands-on expertise with distributed data processing frameworks (e.g., Apache Spark, Hadoop) and real-time data streaming technologies (e.g., Kafka, Kinesis, Spark)
• Track record of collaborating with cross-functional teams (engineering, analytics, business stakeholders) to deliver impactful data solutions
• Experience evaluating, selecting, and integrating emerging data technologies and tools to improve scalability, security, and performance
• Published contributions to open-source projects, technical blogs, or industry forums related to data engineering
• Advanced degree in Computer Science, Engineering, Mathematics, or a related field
• Recognized for mentoring and developing technical talent, fostering a culture of innovation and continuous improvement