Senior IT Business Analyst (AI Enabled Product Engineering)

Life Science Connect•Horsham Township, PA

15d•$135,000 - $155,000

About The Position

Life Science Connect is redefining industry standards by transitioning from a traditional B2B publisher into a modern data authority. We specialize in constructing dedicated platforms, identity frameworks, and intelligence layers engineered to speed up development for life sciences experts. Our strategic investments in decoupled modern architecture and AI-enhanced engineering workflows empower our teams to rapidly deploy scalable, premium-quality products. We are seeking a Senior IT Business Analyst who excels at bridging product strategy, engineering, data, and AI-augmented development. This position involves being deeply integrated into a high-velocity engineering organization, partnering with Product, Engineering, Data, DevOps, and UX to turn strategic concepts into precise, actionable technical architectures. The role focuses on decoding complex business challenges into clear, implementation-ready specifications, utilizing AI tools to expedite analysis, and maximizing the time engineers spend writing code rather than untangling ambiguity. The Senior IT Business Analyst plays a critical role in ensuring engineers spend their time building innovative solutions—not deciphering requirements. This role is ideal for someone who enjoys solving complex business problems, partnering closely with engineers, embracing AI as a productivity multiplier, and helping shape the next generation of intelligent digital products.

Requirements

5+ Years of Business Analysis Experience
Experience working closely with engineering teams building SaaS platforms, digital products, or enterprise applications and knowing how to bridge business needs with technical execution.
Technical Fluency: Comfortable discussing and documenting REST APIs and JSON payload structures, Headless CMS architectures and decoupled front-ends, Identity, Authentication, and Customer Identity frameworks (CIAM), Relational and modern cloud data models (Snowflake), Event Tracking, telemetry, and Customer Journey Analytics, Cloud-based application architectures and system integrations.
AI-Augmented Analyst: Actively use AI to scale daily output as a technical sparring partner for requirements generation, gap analysis, user story refinement, edge-case discovery, and workflow optimization. Ability to engineer effective prompts and possess the critical engineering mindset required to validate, audit, and correct AI outputs.
Product Mindset: Understand that every requirement should tie directly to customer value, operational efficiency, measurable business outcomes, or revenue growth.
Systems Thinker: Enjoy untangling complex processes, identifying hidden technical dependencies, simplifying workflows, and designing scalable architectural solutions.
Excellent Communicator: Ability to confidently facilitate alignment between executives, business stakeholders, designers, engineers, and data teams while adapting communication style.
Highly Autonomous: Excel in distributed teams where asynchronous documentation, radical ownership, proactive communication, and independent problem-solving are essential.

Nice To Haves

Experience with Next.js, React, or modern JavaScript frameworks.
Agile/Scrum product development in highly iterative environments.
Technical Business Analysis within cloud-native software engineering organizations.
Headless CMS platforms (Optimizely preferred).
Snowflake or modern cloud-native data platforms.
Customer Identity and Access Management (CIAM) systems.
Product analytics, event streaming, and customer journey mapping.
API integration, contract design, and webhook architecture.
AI-assisted software delivery tools and prompts.
Jira, Confluence, and structured ticket workflows.
Lucidchart, Miro, or similar visual modeling tools.

Responsibilities

Translate Strategy into Engineering Execution: Partner with Product Leadership and Engineering to transform strategic initiatives into clear technical requirements, user stories, workflows, APIs, and acceptance criteria that engineers can immediately build from.
Drive AI-Augmented Analysis: Use modern AI tools to rapidly produce requirements, process models, technical documentation, and data mappings while maintaining exceptional structural accuracy and logical consistency.
Design Intelligent Product Workflows: Analyze complex business processes and design scalable workflows that leverage identity, personalization, analytics, AI services, and customer intelligence across our product ecosystem.
Own Product Specifications: Produce implementation-ready documentation including PRDs, functional specifications, user stories with rigorous acceptance criteria, API contracts, process flows, and data mappings.
Partner Deeply with Engineering: Work directly alongside Full Stack Engineers to clarify requirements, answer implementation questions, refine technical solutions, and remove ambiguity before development begins.
Enable Data Intelligence: Collaborate with Data Engineering to define modern data models, ingestion patterns, warehouse requirements, reporting needs, and customer intelligence workflows while ensuring data quality throughout the product lifecycle.
Validate What We Build: Support testing, feature validation, and release readiness by ensuring delivered functionality aligns perfectly with business objectives and technical specifications.
Improve How We Work: Continuously improve documentation standards, Agile practices, AI-enabled delivery workflows, and product discovery processes to help the entire engineering organization move faster with greater confidence.