Data Engineer/Assoc. Commercial Data Architect

Incyte Corporation•Chadds Ford Township, PA

About The Position

A global biopharmaceutical company on a mission to Solve On, Incyte follows science to find solutions for patients with unmet medical needs. Through the discovery, development, and commercialization of proprietary therapeutics, Incyte has established a portfolio of first-in-class medicines for patients and a strong pipeline of products in Hematology, Oncology and Inflammation and Autoimmunity Headquartered in Wilmington, Delaware, Incyte has operations in North America, Europe, and Asia. We're looking for a Data Engineer / Associate Commercial Data Architect—someone who thinks architecturally but isn't afraid to get into the weeds. This role will be a key member of the MDM/Data Engineering team within the US Business Analytics function. You'll work at the intersection of data engineering, business strategy, and analytics, helping translate commercial goals into scalable, well-governed data solutions on our modern cloud stack. This is an early-career role built for someone who has developed strong foundational skills in data engineering or analytics and is ready to grow into an architectural mindset. You'll be supported by senior leaders across the MDM/Data Engineering team while taking real ownership over data design decisions that directly impact revenue, sales effectiveness, and customer outcomes.

Requirements

3+ years of experience in data-related roles (data engineering, analytics engineering, BI, or similar).
Bachelor’s Degree in technical area (Computer Science, Business Information Systems, Engineering or related specialty)
Strong knowledge of data modeling, SQL, and modern data platforms (e.g., databricks, cloud data warehouses).
Knowledge of commercial application architecture and the data and process required to support them (CRM, MDM, Reporting Platforms)
Hands-on experience with SQL and at least one modern cloud data platform (Databricks (preferred), Azure Synapse, Snowflake, or similar)
Exposure to ETL/ELT pipeline development and data modeling concepts (star schema, normalized models, lakehouse patterns)
Some experience working across business stakeholders, not just in the technical stack
Experience with business-facing pharmaceutical data (Xponent Plantrak, 867, specialty pharmacy, marketing, finance, or customer data).
Experience creating BRDs, functional specs, data dictionaries, or process diagrams.
Demonstrated track record of aligning data initiatives with business outcomes.
Ability to communicate clearly with both technical and non-technical stakeholders.
Strong problem-solving skills and a willingness to learn the “why” behind business requests.

Responsibilities

Translate Business Needs into Data Architecture: Partner with senior data leaders and commercial teams (sales, product strategy, market access, finance) to understand business goals and help design data models that directly support them.
Document solutions through BRDs, functional specs, data dictionaries, entity-relationship diagrams, and process flows—making the implicit explicit for both technical and business audiences.
Design for Usability, Not Just Elegance: Develop data solutions that are clear, scalable, and aligned with KPIs.
Support the design of a scalable business-facing data layer that translates complex data into consistent, trusted metrics—enabling self-service analytics and aligning teams around a common understanding of performance.
Work within Databricks and Azure to design Lakehouse patterns, medallion architectures, and structured SQL Server schemas that serve both engineering and business consumers.
Own Commercial Data Domains: Assist in managing and improving datasets across customers, sales effectiveness, master data, and specialty pharmacy—ensuring consistency, lineage, and reliability.
Help structure data for dashboards, forecasting pipelines, and performance tracking systems used by leadership and field teams.
Support integration and governance of pharmaceutical data sources (e.g., IQVIA Xponent, 867 data, specialty pharmacy feeds).
Bridge the Gap Between Technical and Business Teams: Act as a clear communicator across data engineers, analysts, third-party vendors, and non-technical business stakeholders.
Help validate requirements, clarify ambiguity, and confirm that what gets built answers the original question.
Bring an 'explain it simply' mindset to technical concepts without dumbing them down.
Uphold Data Quality and Governance: Implement data standards, naming conventions, and quality controls that enable—not slow down—business innovation.
Help identify and resolve data quality issues early in the pipeline, not at the dashboard.