Senior Manager of Software Engineering - Data Engineer

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Senior Manager of Software Engineering - Data Engineer

Hyderabad, Telangana, India

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Job Information

• Job Identification210758222
• Job CategorySoftware Engineering
• Business UnitConsumer & Community Banking
• Posting Date07/10/2026, 02:56 AM
• LocationsMAGMA,UNIT-1,PHASE-IV,SY NO.83/1,PLOT NO 2, GR Floor TO 2 Floor and 5 Floor TO 16 Floor,Basement 1,2, Hyderabad, IN-TG, 500081, IN
• Apply Before07/31/2026, 12:00 AM
• Job ScheduleFull time

Job Description

When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.

As a Senior Manager of Software Engineering at JPMorgan Chase within the Consumer and community banking team, 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 functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Job Responsibilities

• Leads technology and process implementations to achieve functional technology objectives.
• Provides guidance to immediate team of software engineers on daily tasks and activities
• Holds accountability decisions that influence teams’ resources, tactical operations, and the execution and implementation of processes and procedures.
• Carries governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations.
• Delivers technical solutions that can be leveraged across multiple businesses and domains; influences peer leaders and senior stakeholders across the business, product, and technology teams.
• Sets the overall guidance and expectations for team output, practices, and collaboration; manages stakeholder relationships and the team’s work in accordance with compliance standards, service level agreements, and business requirements
• Anticipates dependencies with other teams to deliver products and applications in line with business requirements
• Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
• Applies 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

• 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
• Ability to lead scrum team and work with product owners & stakeholders to build an executable backlog.
• Experience in building applications with high focus on automation across SDLC (CI/CD, AFTs, performance, resiliency & scalability) on AWS, hybrid and on premise platforms.
• Experience with Big Data / Distributed / cloud technology (AWS Big data services like lambda, glue, glue emr and Spark Architecture, Performance tuning ,Spark SQL, Streaming, KAFKA, Entitlements etc., )
• Experience in JAVA full stack
• Extensive experience in building performant APIs to process high volume payloads.
• Extensive experience in domain driven design, data modeling, micro services framework, event & streaming processes.
• Experience developing and leading cross-functional teams of technologists.
• Experience in Data Management, Data Catalog and Data Governance domains.
• 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.

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