Senior Manager of Data Engineering

Lead teams to deliver scalable, secure data solutions that power analytics, reporting, and AI/ML across the firm.

Join us and shape the future of data engineering at JPMorganChase. You will have the opportunity to drive innovation, accelerate adoption of cutting-edge platforms, and empower teams to deliver impactful data solutions. We value your leadership and technical expertise, and offer an environment where you can grow your career and make a real difference. Be part of a collaborative team that values diverse perspectives and continuous learning.

As a Senior Manager of Data Engineering at JPMorgan Chase within Enterprise Platforms, you will lead teams responsible for onboarding enterprise data and delivering scalable data pipelines on the Palmos platform. You will provide technical leadership and strategic direction to accelerate adoption of Palmos, enabling domain-driven data onboarding and delivery of curated data products aligned with our data mesh architecture.

You will play a key role in enabling analytics, reporting, and AI/ML use cases across the firm. Your work will help shape our approach to data engineering and drive impactful outcomes for the business.

Job Responsibilities:

Provide overall direction, oversight, and coaching for teams of data engineers delivering data onboarding and pipeline solutions

Lead onboarding of enterprise datasets into Palmos, enabling standardized ingestion, transformation, and publishing of curated data assets

Own delivery of scalable data pipelines and workflows using Databricks, Spark, and cloud-native technologies

Ensure alignment to data mesh principles, including domain ownership, self-service enablement, and federated governance

Drive collaboration across platform, domain, governance, and business teams to execute a complex book of work

Define and enforce best practices for data engineering, including data modeling, quality, observability, pipeline reliability, and adoption of platform capabilities and reusable frameworks to improve efficiency and reduce time-to-insight

Implement data security, entitlements, and governance controls to ensure protection of enterprise data

Identify risks, dependencies, and delivery challenges and escalate as needed to ensure successful execution

Enable downstream analytics, reporting, and AI/ML use cases through trusted, high-quality data

Set and scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establish measurable expectations (e.g., throughput, defect reduction, reliability) and ensure consistent validation, security, resiliency, and reuse of proven patterns

Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets

Required Qualifications, Capabilities, and Skills:

Experience leading and scaling data engineering teams and managing delivery portfolios

Hands-on experience with Databricks, Spark/PySpark, Python, and SQL

Strong understanding of modern data architectures (lakehouse, data mesh, distributed systems)

Experience building and maintaining scalable data pipelines and data platforms

Knowledge of cloud platforms (AWS), CI/CD, and software development lifecycle practices

Strong understanding of data governance, security, and access control frameworks

Ability to collaborate effectively across global teams and influence both technical and business stakeholders

Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.

Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.

Preferred Qualifications, Capabilities, and Skills:

Experience with Databricks lakehouse, Delta Lake, and medallion architecture

Familiarity with enterprise data platforms such as Palmos and domain-based data product models

Experience supporting analytics, reporting, and AI/ML workloads with engineered data pipelines

Exposure to data quality, observability, and metadata-driven pipeline frameworks

Experience working within EU regulatory and data protection environments (e.g., GDPR)