You have the opportunity to unleash your full potential at a world-renowned company and take the lead in shaping the future of technology.

As a Senior Manager of Data Engineering at JPMorgan Chase within Asset and Wealth Management, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other data users within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Job responsibilities

Architects the design of complex data solutions that meet diverse business needs and customer requirements and guides the evolution of logical and physical data models to support emerging business use cases and technological advancements.

Builds and manages end-to-end cloud-native data pipelines in AWS, leveraging hands-on expertise with AWS components, analytical systems from the ground up, providing architectural direction, translating business issues into specific requirements, and identifying appropriate data to support solutions.

Works across the Service Delivery Lifecycle on engineering major/minor enhancements, ongoing maintenance of existing applications and conducts feasibility studies, capacity planning, and process redesign/re-engineering of complex integration solutions.

Helps others build code to extract raw data, coach the team on techniques to validate its quality, and apply deep data knowledge to ensure the correct data is ingested across the pipeline.

Guides the development of data tools used to transform, manage, and access data, and advise the team on writing and validating code to test the storage and availability of data platforms for resilience.

Oversees the implementation of performance monitoring protocols across data pipelines, data accessibility within assigned pipelines, coaches the team on building visualizations and aggregations to monitor pipeline health, implements solutions and self-healing processes that minimize points of failure across multiple product features.

Prepares team members for meetings with appropriate stakeholders across teams, addresses concerns around data requirements by providing guidance on feature estimation and leverages expertise to mentor and enhance team capabilities.Collects, refines, and transforms data accurately from diverse sources using advanced SQL queries and Alteryx expertise.Designs, develops and manages dynamic data visualization solutions like Tableau and ThoughtSpot providing actionable insights for informed decision-making.Publishes and manages dashboards, reports with optimized scheduling, addressing data discrepancies and performance issues proactively.Defines critical data scope within products, documenting, classifying, and enriching data with comprehensive metadata for effective use.

 

Required qualifications, capabilities, and skills

 

Formal training or certification on software engineering concepts and 5+ years applied experience. In addition, 2+ years of experience leading technologists to manage and solve complex technical items within your domain of expertise.Extensive experience in managing the full lifecycle of data, from collection and storage to analysis and reporting.Experienced in SQL, with the ability to design and optimize complex queries and database structures.Deep understanding of NoSQL databases and their strategic applications within the industry.Proven track record in statistical data analysis and the ability to derive actionable insights from complex data sets.Experience in leading large-scale data engineering projects and implementing custom solutions to meet business objectives.Experience in analytics/business intelligence to deliver data-driven insights and strategic solutions, with mandatory hands-on expertise in Alteryx, SQL, Tableau for advanced analytics, complex data manipulations, and crafting advanced data visualizations.Demonstrated ability to build and manage cloud-native data pipelines in AWS, with hands-on knowledge of AWS components.

 

Preferred qualifications, capabilities, and skills Proficient in Python to effectively meet future and evolving data needs, while adeptly tackling complex data logic challenges and designing sophisticated workflows for problem-solving.Drive projects efficiently using extensive experience with tools like JIRA and Confluence, demonstrating agility and adaptability to transition swiftly between projects and meet evolving demands.Exhibit exceptional written and verbal communication skills to articulate complex ideas clearly and persuasively to diverse audiences  with assertive communication to set and manage stakeholder expectations under tight deadlines.Extensive experience with major cloud platforms such as AWS, Azure, and Snowflake.Proficiency in ETL tools, including PySpark, Snowflake, and other data processing frameworks.Strong understanding of Data Mesh, data modeling, and domain-driven design principles.Experience with version control systems and tools, including Git, GitHub, GitLab, and Bitbucket.