IntegriChain

• Own and evolve the multi-quarter product strategy and roadmap (6+ quarters) for assigned product areas, aligned to company objectives and market needs. • Facilitate prioritization with stakeholders using structured frameworks (e.g., value vs. effort, risk reduction, strategic alignment). • Lead discovery activities with customers, internal users, and domain experts to validate problems before solutions. • Translate validated needs into epics, features, and user stories with clear acceptance criteria and business context to explain the business need and value. • Proactively identify delivery risks, dependencies, and scope changes, and drive resolution. • Lead release planning and readiness for assigned product areas, ensuring features are usable, valuable, and supportable. • Facilitate roadmap reviews, prioritization discussions, and decision forums with senior leaders. • Represent the product in customer conversations, translating feedback into actionable insights and decisions. • Collaborate with delivery teams to estimate roadmap initiatives and inform quarterly and annual planning. • Align priorities with capacity constraints and support resource allocation discussions. • Coach and mentor business analysts on best practices in discovery, prioritization, and execution.

• Lead/execute a project workstream with limited day-to-day supervision • Gather requirements for and proactively design complex solutions to provide business goals • Partner closely with the client business team to facilitate enhancement request requirements definition • Liaise between client and IntegriChain’s Product Engineering group to ensure requirements are understood • Review new client business/contracting scenarios • Support client in making contract configuration or other systems changes needed to support the scenario, including testing of those changes • Support project plans, status reports and other project documentation • Oversee development of engagement deliverables • Leverage domain expertise and data-driven reasoning to provide functional thought leadership internally and to clients • Provide effective communication with internal and clients • Initiate/lead internal projects • Mentor Analysts, Senior Analysts, and Lead Analysts • Participate in the recruiting process as appropriate • Support business development activities

• Lead and manage cross-functional project teams across the full lifecycle of data solutions implementations, from discovery through go-live and transition to managed services. • Serve as the primary point of contact for commercial data providers and pharma manufacturer stakeholders, ensuring alignment of data needs and compliance requirements, including HIPAA and BAA considerations. • Analyze business and data requirements and translate them into detailed project plans, functional documentation, and actionable implementation steps. • At times, document business and technical requirements including documentation of workflows, maps, models, data specifications, visualization requirements, etc. • Oversee delivery of solutions for: Channel and patient data aggregation (standard and custom feeds), Commercial Data Warehouse (CDW) and Master Data Management (MDM) implementations, Data quality validation, harmonization, and transformation workflows, Analytics platforms, dashboards, and reporting tools. • Partner with internal data engineering, product, and client delivery teams to define and prioritize solution requirements and enhancements. • Provide project oversight across managed services, ensuring quality, consistency, and continuous improvement in recurring data operations. • Lead stakeholder meetings and status updates, facilitate issue resolution, and escalate risks as needed to maintain project momentum and customer satisfaction. • Support pre-sales and client scoping efforts by defining implementation approaches, timelines, and resource needs for proposed data solutions. • Leverage deep industry experience and understanding of pharmaceutical data standards to guide solution design and client recommendations.

• Own end-to-end resolution of complex customer data questions and issues; drive cross-functional resolution by anticipating roadblocks and taking action based on impact analysis • Own the preparation, delivery, and follow-through of Quarterly Business Reviews for both corporate and enterprise accounts, including performance data analysis and strategic recommendations • Deliver regular customer satisfaction evaluations and ensure customer satisfaction scores remain at or above the company defined standard; Enhance ongoing data processing and deliver efficiencies through critical analysis of data architecture and usage via applied continuous improvement • Drive process flow analysis and process redesign when applicable • Understand each customer's business needs and pain points, act as our advocate to explain the value IntegriChain services provide, and drive usage of the products to which the customer subscribes • Manage the communication of timelines, change requests, and status reports of all corporate customer associated projects • Proactively identify, escalate, and lead the response to customer churn risks; develop and own action plans to address concerns, leveraging cross-functional teams and data-driven reasoning to influence outcomes; Develop, document, train and update processes as they relate to the customer • Collaborate with subject matter experts, developers and QA teams to manage delivery of newly defined analytics and reports from start to finish • Lead the creation of project plans for custom requests and enhancements; guide internal stakeholders through task execution within assigned modules, track progress, and ensure timely team delivery • Lead training, documentation, and delivery efforts; coach and develop junior team members on best practices across 2–3 modules and share knowledge cross-functionally • Define, document, and gain customer approval for new and enhanced report requirements to provide analytical, reporting and data mart structures that enhance data visualization • Evaluate and recommend new technologies or process change that will enhance the current environment or provide greater business advantage • Coach and develop Customer Engagement Associates on account leadership, complex problem-solving, and customer communication; informally lead project pods or team initiatives as needed • Propose process improvements that eliminate toil within the department

• Manage day-to-day processing, validation, and reconciliation of chargebacks and rebate claims in accordance with contract terms and pricing • Validate membership eligibility, effective dates, and contract alignment for chargeback submissions • Review and process rebate invoices including DSA, GPO admin fees, and indirect/direct agreements • Conduct detailed root cause analysis for chargeback discrepancies and rebate disputes • Work directly with wholesalers, GPOs, and internal teams (Finance, Contracting, Membership) to gather supporting data and resolve open items • Communicate with Finance Shared Services to reconcile open deductions or payment variances • Prepare and maintain documentation, trackers, and audit support across CBK and PBR functions • Participate in internal and customer-facing meetings to provide updates, resolve issues, and support performance discussions • Assist with peer reviews, QA checks, and documentation updates • Support UAT, new customer onboarding, and implementation of new products or pricing setups • Identify and recommend process improvements or automation opportunities

• Lead and manage cross-functional project teams across the full lifecycle of data solutions implementations, from discovery through go-live and transition to managed services. • Serve as the primary point of contact for commercial data providers and pharma manufacturer stakeholders, ensuring alignment of data needs and compliance requirements, including HIPAA and BAA considerations. • Analyze business and data requirements and translate them into detailed project plans, functional documentation, and actionable implementation steps. • At times, document business and technical requirements including documentation of workflows, maps, models, data specifications, visualization requirements, etc. • Oversee delivery of solutions for: Channel and patient data aggregation (standard and custom feeds) Commercial Data Warehouse (CDW) and Master Data Management (MDM) implementations Data quality validation, harmonization, and transformation workflows Analytics platforms, dashboards, and reporting tools • Partner with internal data engineering, product, and client delivery teams to define and prioritize solution requirements and enhancements. • Provide project oversight across managed services, ensuring quality, consistency, and continuous improvement in recurring data operations. • Lead stakeholder meetings and status updates, facilitate issue resolution, and escalate risks as needed to maintain project momentum and customer satisfaction. • Support pre-sales and client scoping efforts by defining implementation approaches, timelines, and resource needs for proposed data solutions. • Leverage deep industry experience and understanding of pharmaceutical data standards to guide solution design and client recommendations.

• Help define and mature data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain. • Design, build, optimize, and operate Snowflake data models, pipelines, stored procedures, and high-volume data processing patterns. • Partner with MDM and Product teams to support HCO Master data ingestion, outbound extracts, cross-reference data, golden record consumption, survivorship outputs, and downstream publishing patterns. • Work with Product, Engineering, MDM, Data Science, DevOps, Security, and business stakeholders to align data solutions to enterprise priorities. • Use dbt or similar ELT tooling to develop reliable, maintainable, testable, and observable data pipelines. • Drive Snowflake performance tuning, warehouse sizing, workload management, cost tracking, and cost optimization practices. • Partner with Data Science leadership to rationalize and consolidate the enterprise data landscape across products, platforms, and acquired capabilities. • Define reusable data integration patterns for batch, micro-batch, near-real-time, and application-to-application data exchange. • Collaborate with cross-functional teams to understand business data needs, source-system realities, and enterprise application integration requirements. • Design scalable patterns for ingesting, transforming, mastering, and publishing data across operational and analytical use cases. • Help establish standards for data contracts, schema evolution, data quality, lineage, and data ownership. • Design and build data pipelines that load source data into Reltio MDM and extract mastered outputs from Reltio for downstream Snowflake, analytics, AI, and operational use cases. • Partner with MDM configuration and Product Management teams to translate HCO mastering requirements into data pipeline, mapping, validation, reconciliation, and publishing patterns. • Work with Reltio APIs, exports, crosswalks/XREFs, event-based integration patterns, and bulk load/extract mechanisms as needed to support inbound and outbound data flows. • Engineer integration patterns for HCO Master data, including party/entity, address, identifier, hierarchy, relationship, match/merge, survivorship, and golden record outputs. • Support source ingestion and reference data integration involving datasets such as HIN, DEA, NPI, NCPDP, 340B/PHS, channel outlet data, customer/account data, and other life sciences master/reference sources. • Develop validation and reconciliation processes to compare source data, Reltio mastered data, Snowflake curated data, and downstream consumption layers. • Help operationalize MDM outputs for business-facing data products, semantic models, reporting tables, APIs, and AI-ready datasets. • Design Snowflake database, schema, table, view, and semantic-layer patterns that support performance, governance, and maintainability. • Optimize Snowflake workloads using clustering, micro-partition awareness, warehouse sizing, query profiling, caching behavior, and workload isolation. • Implement Snowflake cost tracking and optimization practices, including warehouse utilization monitoring, inefficient query identification, and cost allocation by workload, team, or use case. • Build scalable SQL and Snowflake stored procedure logic for large-volume data processing and analytical workloads. • Apply secure Snowflake design patterns including RBAC, masking, access isolation, auditing, and environment separation. • Design, build, and maintain reliable ELT pipelines using dbt or comparable modern data transformation tooling. • Develop Python-based automation for API integration, file processing, metadata management, validation, orchestration support, and operational tooling. • Develop modular, tested, and reusable transformation models for raw, curated, mastered, and business-ready data layers. • Implement automated data quality checks, source freshness checks, reconciliation, logging, and exception-handling patterns. • Build orchestration-ready pipelines that support dependency management, restartability, incremental loads, and operational monitoring. • Collaborate with DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines. • Spearhead logical and physical data modeling efforts for enterprise analytical, operational, MDM, and AI-ready datasets. • Design models that balance normalization, dimensional modeling, medallion/lakehouse concepts, and application-specific consumption needs. • Create denormalized reporting and semantic-model-ready structures that simplify business consumption and reduce ambiguity for AI/LLM use cases. • Process and optimize large data volumes in Snowflake using efficient SQL, PL/SQL-style procedural logic, Snowflake Scripting, and performance-aware design. • Create reusable patterns for historical tracking, snapshots, audit columns, data versioning, and lifecycle management. • Ensure data models support downstream BI, AI/ML, semantic models, data apps, MDM Explorer/Entity 360 use cases, and enterprise reporting.

• Join the Engineering team as a Forward Deployed AI Engineer • Act as a resident AI expert within Individual Departments/Business Units • Function as engineer, solutions architect, and internal consultant • Design and build AI-powered application features using LLM APIs • Create agent loops that can select tools, execute actions, and summarize results • Develop chat-based analytical experiences connecting user questions to backend tools • Improve prompt quality and manage context windows • Use advanced AI coding tools to accelerate development while maintaining code quality • Embed directly with internal departments to identify where AI can drive efficiency • Lead the end-to-end implementation of AI solutions within operational contexts • Provide hands-on troubleshooting and support for AI models running in production mode

• Responsible for designing, building, and delivering product functionalities with high reliability and quality • Participate in refinement to understand, collaborate closely with product, QA, and other teams to translate requirements into technical solutions • Anticipate problems, lead by example, improve engineering practices, and ensure the scalability, performance, and maintainability of the system • Take ownership of major components, review code rigorously, and contribute to the long-term technical roadmap • Build and maintain stable products with latest tech-stack, frameworks and following Agile and DevOps ways-of-working • Accountable for delivering quality solutions to the problems without compromising on the planned deadlines • Leverage various AI models through tools (such as Copilot) for code generation, test case generation, code analysis, and code optimization • Contribute to redesigning and enhancing applications to latest technologies • Play a part in every aspect of the software development lifecycle, including software design, development, testing, reviewing, and deployment

• Join the Data Science team as an AI Data Engineer responsible for building the data foundations that make enterprise AI products accurate, explainable, and scalable • Design and implement Snowflake and dbt pipelines from raw source data to curated gold-layer datasets • Create semantic models that LLM tools can use reliably • Partner with data science, product, and engineering teams to convert data dictionaries and business definitions into AI-ready data products