Senior Specialist, Data Engineering

Position Overview 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. Required Qualifications 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 Qualifications 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. Salary & Benefits Salary range: $106,200.00 – $167,200.00. The successful candidate will be eligible for annual bonus and long‑term incentive, if applicable. We offer a comprehensive package of benefits including medical, dental, vision, retirement benefits (401(k)), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at Job Details US and Puerto Rico Residents Only. San Francisco Residents Only – We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance. Los Angeles Residents Only – We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance. Employee Status: Regular. Relocation: Domestic. VISA Sponsorship: No. Travel Requirements: 10%. Flexible Work Arrangements: Hybrid. Equal Employment Opportunity As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit: EEOC Know Your Rights, EEOC GINA Supplement. Learn more about your rights, including under California, Colorado and other U.S. State Acts. #J-18808-Ljbffr Merck & Co.