Salesforce Developer

Workday (Software) Data Visualization Workflow Management Systems Engineering Metadata Management Workflow Automation Microsoft Dataverse Software Development Microsoft SharePoint Cycle Time Variation Contingent Workforce Lifecycle Management Atlassian Confluence Business Intelligence Translational Medicine IT Capacity Management Operational Data Store Artificial Intelligence Technical Documentation Data Quality Assessment Management By Exception Microsoft Power Platform Self Service Technologies SQL (Programming Language) Extract Transform Load (ETL) Scrum (Software Development) Python (Programming Language) Good Laboratory Practice (GLP) Systems Development Life Cycle Spotfire (Data Analytics Software) Defining Roles And Responsibilities Low-Code Development Platform (LCDP) Application Programming Interface (API), The Data Engineer is a key member of the Discovery Operations team, responsible for independently owning the intake, prioritization, delivery, and adoption of scalable data products, workflow automations, and decision-ready insights across the Discovery, Preclinical, and Translational Medicine (DPTM) Ops portfolio - spanning capital asset life cycle management, regulatory and safety compliance, externalization, site operations, and lab support. This role strengthens our ability to operate in an increasingly digital environment by consolidating fragmented data, building data connections across heterogeneous systems, reducing manual and repetitive work with automations, and enabling data-driven insights (including AI-assisted approaches) from interactive reporting, modeling, and simulation tools that support day-to-day decisions and review-by-exception management. The position serves as a hands-on builder and cross-functional facilitator across the network to deliver secure, sustainable solutions aligned to organizational priorities.

This role not only delivers solutions, but also identifies demands from the labs and DPTM Ops needs, and works a governance and prioritization process to build a portfolio of projects agreed on by DPTM Ops LT. Beyond project planning and execution, this role is vital for interfacing with business stakeholders to drive adoption through training, communication, and continuous improvements based on user feedback and metrics. The role is also a key interface with IT groups to ensure continuity and technology advancements for current and future tools.

Responsibilities

• Lead demand intake with DPTM Ops stakeholders; shape ambiguous needs into clear problem statements, success measures, and prioritized use cases; maintain a transparent backlog/roadmap and communicate tradeoffs.
• Prototype rapidly (proofs of concept) and mature successful solutions into supportable products with defined owners, documentation, monitoring, and governance checkpoints aligned to IT SDLC, compliance, and continuous support expectations.
• Design, build, and optimize data pipelines and transformations to consolidate operational data from multiple sources into high-quality, analytics-ready datasets.
• Implement data quality checks, refresh/monitoring routines, and clear documentation so datasets are reliable, reusable, and easy to adopt.
• Design, build, and maintain interactive dashboards (e.g., Power BI, Spotfire, or other visualization tools) and/or custom applications that deliver integrated views of organizational health, utilization, and operational KPIs.
• Develop lightweight models, simulations, or trending indicators to support prioritization, capacity planning, and ROI narratives (e.g., time saved, reduced errors, cost avoidance).
• Identify high-friction manual activities and implement automations (workflow, database-driven, and where appropriate AI-assisted/agentic approaches) to reduce cycle time and enable review-by-exception.
• Maintain and enhance existing tools; continuously improve usability, performance, and scalability.
• Facilitate cross-functional alignment (RaDS IT, AMS, digital/product teams, and business SMEs) to select platforms, define roles/hand-offs, manage dependencies/risks, and ensure solutions are secure, scalable, and supportable.
• Drive adoption and digital fluency for DPTM Ops: create enablement materials, run training/office hours, track usage and stakeholder feedback, and retire manual processes as capabilities mature., Workday (Software) Data Visualization Workflow Management Systems Engineering Metadata Management Workflow Automation Microsoft Dataverse Software Development Microsoft SharePoint Cycle Time Variation Contingent Workforce Lifecycle Management Atlassian Confluence Business Intelligence Translational Medicine IT Capacity Management Operational Data Store Artificial Intelligence Technical Documentation Data Quality Assessment Management By Exception Microsoft Power Platform Self Service Technologies SQL (Programming Language) Extract Transform Load (ETL) Scrum (Software Development) Python (Programming Language) Good Laboratory Practice (GLP) Systems Development Life Cycle Spotfire (Data Analytics Software) Defining Roles And Responsibilities Low-Code Development Platform (LCDP) Application Programming Interface (API) +0

Coaching Power BI Dashboard Usability Scheduling Operations Management Automation Governance Innovation Compassion Agentic AI Scalability Procurement Simulations Data Quality Data Science Communication Data Security Data Modeling Biotechnology User Feedback Prioritization Capital Assets Data Pipelines Pharmaceuticals Test Automation Data Governance Database Design Data Validation Project Planning Computer Science Microsoft Access Machine Learning Telephone Skills Data Engineering Digital Literacy Agile Methodology Appian (Software), Required

• Minimum requirement of a bachelor's degree in computer science, data engineering, information systems, engineering, or a related quantitative discipline; equivalent experience considered.
• 3+ years of progressive experience in data engineering, analytics, workflow automation, or a related discipline.

Preferred

• Strong SQL skills and data modeling fundamentals; ability to design analytics-ready datasets that support reporting and integrated views.
• Proficiency in Python for data engineering and automation (e.g., ETL/ELT, APIs, data validation, orchestration); ability to build maintainable code with tests.
• Experience designing and supporting data pipelines and integrations across heterogeneous systems; familiarity with common patterns (batch, incremental loads, scheduling, monitoring).
• Dashboarding and data visualization experience (Power BI preferred; comparable tools acceptable) with an emphasis on usability, performance, and governance.
• Experience with cloud platforms and secure data access patterns.
• Version control and documentation best practices (clear technical documentation in Confluence or similar).
• Experience with Appian business orchestration development and troubleshooting.
• Experience with SharePoint creation and updating.
• Familiarity with agentic/AI-assisted automation approaches (e.g., LLM-enabled reporting or triage) with appropriate controls, privacy, and validation.
• Experience with Microsoft Power Platform, Dataverse, or comparable low-code platforms and their integration patterns.
• Experience building lightweight internal applications (e.g., Python web apps) to enable workflow automation and self-service data access.
• Proven ability to translate stakeholder needs into well-scoped deliverables, manage a backlog, and communicate tradeoffs; comfortable working as a liaison with IT/support partners (e.g., AMS, platform/product teams).
• Demonstrated ability to operate independently in ambiguous environments: define operating models, facilitate governance forums, and drive stakeholder alignment/adoption across multiple functions.
• Change management and enablement skills: coaching users, driving adoption, and deprecating manual/spreadsheet-driven processes.
• Experience in pharmaceutical, biotech, or other regulated environments.
• Domain experience with operational workflows, procurement/finance reporting, equipment management, or regulated environments (e.g., GLP/GxP concepts).
• Experience with Agile/Scrum delivery methodologies in a data or analytics context.
• Familiarity with data cataloging, metadata management, or data governance frameworks.
• Exposure to machine-learning techniques and the practical use of AI.

Required Skills: Biopharmaceutical Industry, Business Intelligence (BI), Database Design, Data Engineering, Data Governance, Data Modeling, Data Quality, Data Quality Assessments, Data Quality Control, Data Quality Test Automation, Data Science, Data Visualization, Global Health, Machine Learning (ML), Metadata Management, Operating Models, Software Development, Stakeholder Relationship Management

Preferred Skills: Analytics, Automation, Automation Technology, Project Management, Usability Scheduling Operations Management Automation Governance Innovation Compassion Agentic AI Scalability Procurement Simulations Data Quality Data Science Communication Data Security Data Modeling Biotechnology User Feedback Prioritization Capital Assets Data Pipelines Pharmaceuticals Test Automation Data Governance Database Design Data Validation Project Planning Computer Science Microsoft Access Machine Learning Telephone Skills Data Engineering Digital Literacy Agile Methodology Appian (Software)

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The salary range for this role is $106,200.00 - $167,200.00

Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.