{"id":1221739,"url":"https://alion.io/job/the-infatuation-lead-software-engineer-reference-data-engineering","title":"Lead Software Engineer - Reference Data Engineering","company":{"id":1048574,"name":"The Infatuation","domain":"theinfatuation.com","url":"https://alion.io/company/the-infatuation","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":199,"ghost_share":0.015,"stale_share":0,"repost_share":0.221,"time_to_fill_p50_days":3,"computed_at":"2026-09-28T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","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":["Chicago, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":113000,"max_usd":222000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":246},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Maven","optional":false},{"name":"RabbitMQ","optional":false},{"name":"Rest API","optional":false},{"name":"Self-Healing","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"Spring Boot","optional":false},{"name":"Spring Cloud","optional":false},{"name":"Spring Framework","optional":false},{"name":"Spring Security","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-09-22T19:15:43Z","employer_posted_date":"2026-09-22","last_verified_at":"2026-09-29T00:43:14Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"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 at JPMorganChase within the Corporate Sector's Reference Data Engineering, you are 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. This group powers one of the teams' most critical data-driven capabilities through a modern data mesh architecture delivering curated, domain-driven data products in real-time across the firm. Operating at enterprise scale across AWS, Databricks, and on-premises, Reference Data Integration (RDI) manages multi-tenant data delivery with managed-service enablement. The platform uses event-driven streaming (Kafka, Kinesis, Spark Structured Streaming) for high-performance real-time data delivery and is evolving AI/ML-driven data quality and reconciliation capabilities. Join our engineering team to architect intelligent, self-healing data platforms driving the next generation of financial infrastructure\nJob responsibilities\nExecutes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems\nDevelops secure and high-quality production code, and reviews and debugs code written by others\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.\nExecutes creative software solutions through innovative system design and development. Approaches complex data infrastructure challenges with ability to think beyond conventional approaches\nDevelops secure, high-quality production code. Reviews and debugs code written by others. Maintains and elevates engineering standards\nDesigns and develops Java/Spring Boot microservices for real-time data product delivery and platform capabilities\nOwns assigned features end-to-end: requirements, design, implementation, testing, deployment, and monitoring\nOptimizes performance, scalability, and cost efficiency of microservices and data pipelines while writing comprehensive tests (unit, integration, end-to-end) to ensure platform reliability and data integrity\nParticipates in design and code reviews. Proactively identifies and remediates technical debt and helps with production support and incident response. \nContributes to team knowledge sharing: documentation, runbooks, tech talks\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineering concepts and 5+ years applied experience\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.\nHands-on practical experience delivering system design, application development, testing, and operational stability\nExpert-level Java proficiency. Deep knowledge of Spring Boot, Spring Cloud, Spring Data, and Spring Security (JWT/OAuth2), Spring Framework, Spring Boot, and AWS Services in public cloud infrastructure, with experience building cloud-native or cloud-ready applications using AWS\nStrong understanding of distributed systems, microservices architecture, and design patterns\nProduction-level experience with AWS: EC2, S3, Lambda, CloudWatch, IAM, Kinesis. Hands-on with databases: NoSQL (MongoDB), columnar/analytics (Snowflake, Databricks), relational SQL\nExperience with version control (Git/Bitbucket), CI/CD pipelines, and modern DevOps practices (Docker, Kubernetes, Terraform) so you can be proficient in all aspects of the Software Development Life Cycle (SDLC), agile methodologies, and continuous delivery\nProficiency in Java/J2EE and REST APIs. Experience building event-driven Microservices and Kafka (Kinesis, Spark Structured Streaming) and its event-driven architecture and message brokers (Kafka, RabbitMQ)\nHands-on experience with system design, application development, testing with proficiency in GIT/Bitbucket, JIRA, Maven\nAbility to tackle design and functionality problems independently with little to no oversight. Clear articulation of technical concepts in a self-motivated role that requires high ownership of work quality and delivery\nPreferred qualifications, capabilities, and skills\nPython with data engineering experience including exposure to Databricks and a a background in data infrastructure or reference data platforms\nExperience in Platform or Product Development and contribution to open-source projects or public technical content\nAWS Certifications (AWS Certified Solutions Architect - Associate or higher)\nExperience with Spring Cloud (Netflix OSS stack: Eureka, Zuul, Hystrix)\nExperience building or maintaining high-scale, real-time data systems. Familiarity with data product delivery and data mesh patterns\nExperience in multi-region or disaster recovery scenarios\nFamiliarity with managed service architecture patterns","description_format":"text","description_chars":6033,"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":["Food & Beverages","Meat & Poultry","Grains, Cereals & Pasta"],"lifecycle":[{"event":"open","at":"2026-09-25T12:17:36Z"}],"liveness":{"score":31,"band":"fade","label":"Fading","p_open":1,"p_active":0.692,"p_room":0.45,"age_days":5,"expected_fill_days":3,"reasons":["conf:1","velocity","win:tail","comp:brand"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/the-infatuation-lead-software-engineer-reference-data-engineering","json_url":"https://alion.io/job/the-infatuation-lead-software-engineer-reference-data-engineering.json","meta":{"generated_at":"2026-09-29T03:43:07Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3467,"day_limit":5000,"remaining_today":1533,"minute_limit":60,"resets_at":"2026-09-30T00:00:00Z"}}}