{"id":3961643,"url":"https://alion.io/job/jpmorganchase-senior-lead-software-engineer-data-ai-platform-engineer-2","title":"Senior Lead Software Engineer - Data & AI Platform Engineer","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":88,"open_postings":1024,"ghost_share":0.012,"stale_share":0.615,"repost_share":0.047,"time_to_fill_p50_days":15,"computed_at":"2026-11-10T06:52:35Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Jersey City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","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":"Kubernetes","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"Tableau","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-11-05T16:47:27Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-11T02:55:11Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"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 Senior Lead Software Engineer at JPMorgan Chase, within the Commercial & Investment Banking's Payments Technology team to build a Data & AI Platform powering analytics and automation at scale. You’ll design and implement modern data pipelines and platform capabilities on a cutting-edge stack, backed by strong governance across catalog, lineage, and data quality. You’ll lead an Agentic AI initiative to automate and optimize platform engineering and architecture, partnering with product and analytics teams to deliver production-grade solutions.\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\n\nDesigns, builds, and maintains scalable data pipelines and ETL/ELT workflows for batch and real-time processing using Spark, Airflow, Kafka, and Flink\n\nDevelops data platform components including data cataloging, data quality frameworks, and semantic/metrics layers with embedded governance, lineage, and compliance standards\n\nImplements data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption\n\nPartners with analytics teams, product managers, and business stakeholders to translate data requirements into production-grade solutions\n\nDevelops secure high-quality production code, and reviews and debugs code written by others\n\nIdentifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems\n\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\n\nLeads development of the Agentic Autonomous Lakehouse capability - automating governed self-service pipeline provisioning and lakehouse operations (health/cost/performance analysis, best-practice enforcement)\n\nTackles highly complex, ambiguous engineering problems spanning multiple systems and teams, applying deep technical judgment to drive solutions through simplified, modular design and implementation.\n\nEvangelizing engineering patterns, reusable frameworks, and best practices to influence stakeholders and drive adoption of scalable strategic solutions\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineering concepts and 5+ years of applied experience\n\nHands-on practical experience delivering system design, application development, testing, and operational stability\n\nDemonstrated professional experience focused on software engineering or data platform development\n\nAdvanced in one or more programming languages(s); Python, Java and SQL\n\nHands-on experience with distributed data processing frameworks such as Apache Spark and Flink\n\nSolid understanding of data modeling techniques (star schema, snowflake) and query optimization\n\nExperience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow\n\nProficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)\n\nExperience engineering production-grade data platforms on Kubernetes with open catalog integration (e.g., Apache Iceberg, Unity Catalog, OpenMetadata) for scalable data discovery, lineage, and governance.\n\nAdvanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security\n\nExperience developing Agentic AI, LLMs, RAG architectures, MCP, vector databases, and embedding-based retrieval systems\n\nPreferred qualifications, capabilities, and skills\nHands-on familiarity with Data Platform and transformation framework development\n\nExperience with data mesh or data product architectures\n\nProficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes)\n\nExperience with data observability, quality, and metadata management tools\n\nExperience with semantic layers, metrics stores, or BI platforms (Tableau, dbt 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