{"id":1268009,"url":"https://alion.io/job/jpmorganchase-lead-software-engineer-data-and-payments-data-platform","title":"Lead Software Engineer - Data and Payments Data Platform","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":464,"ghost_share":0.009,"stale_share":0.084,"repost_share":0.127,"time_to_fill_p50_days":10,"computed_at":"2026-10-12T06:55:09Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":165000,"max_usd":334000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":861},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Flink","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"RAG","optional":true},{"name":"Tableau","optional":true},{"name":"Terraform","optional":true}],"status":"closed","first_seen_at":"2026-09-25T19:22:23Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-10T14:27:56Z","board_verified":false,"closed_at":"2026-10-10T14:27:56Z","days_open":14,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":14},"description":"We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.\nAs a Lead Software Engineer - Data and Payments Data Platform at JPMorgan Chase within the Commercial and Investment Banking - Data Analytics Payment team, youare an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.\nJob responsibilities\nExecutes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems\nDesigns, builds, and maintains scalable data pipelines and ETL/ELT workflows for batch and real-time processing using Spark, Airflow, Kafka, and Flink\nDevelops data platform components including data cataloging, data quality frameworks, and semantic/metrics layers with embedded governance, lineage, and compliance standards\nImplements data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption\nDrives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation\nPartners with analytics teams, product managers, and business stakeholders to translate data requirements into production-grade solutions\nDevelops secure high-quality production code, and reviews and debugs code written by others\nIdentifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems\nLeads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture\nLeads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies\n\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineering concepts and 5+ years of applied experience\nHands-on practical experience delivering system design, application development, testing, and operational stability\n3+ years of professional experience focused on data engineering or data platform development\nAdvanced in one or more programming languages(s); Python, Java and SQL\nHands-on experience with distributed data processing frameworks such as Apache Spark and Flink\nSolid understanding of data modeling techniques (star schema, snowflake) and query optimization \nExperience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow\nProficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)\nDemonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.\nStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices\nProficient in all aspects of the Software Development Life Cycle and demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)\nPreferred qualifications, capabilities, and skills\nExposure to LLMs, RAG architectures, vector databases, and embedding-based retrieval systems\n\nExperience with data mesh or data product architectures\nProficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes)\nExperience with data observability, quality, and metadata management tools\nExperience with semantic layers, metrics stores, or BI platforms (Tableau, dbt Metrics)","description_format":"text","description_chars":4855,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Wealth Management & Financial Advisors","Asset Management & Funds","Investment Banking & M&A 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