Manager of Data Engineering

JPMorgan Chase · Dublin

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)

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

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

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