Sr. Manager, Analytics Engineering & BI Architecture

The businesses of Merck KGaA, Darmstadt, Germany•Boston, MA

10d

About The Position

The Sr. Manager, Analytics Engineering & BI Architecture is a hands-on technical leader responsible for architecting analytical data models, designing scalable cloud-based data layers, and delivering high-impact Tableau dashboards that directly support business decision-making. This role requires deep expertise in data modeling and transformation within Redshift and Snowflake, ownership of the semantic and extract layers powering Tableau, and the ability to translate complex stakeholder needs into intuitive, performance-optimized, executive-ready analytics products. The role partners closely with business stakeholders, data engineering, analytics teams and IT team to ensure data solutions are accurate, trusted, scalable, and aligned to evolving business priorities. Success in this role requires strong architectural judgment, advanced Tableau design skills, and the ability to balance speed, quality, and governance. You will lead, inspire, and engage with passion by modeling EMD Serono behaviors and working with a sense of urgency to achieve organizational goals.

Requirements

Bachelor’s degree in Computer Science, Engineering, Analytics, or a related technical discipline (or equivalent practical experience).
5+ years of experience in data warehousing, BI, or analytics engineering roles.
5+ years of hands-on experience designing and implementing data models in cloud-based data warehouse (such as Redshift, Snowflake, Databrick, etc.) for analytics and BI use cases.
5+ years of advanced Tableau (Tableau Cloud) development experience, including dashboard architecture, performance tuning, and complex data sources.
Strong SQL expertise, including writing and optimizing complex queries for analytical workloads.

Nice To Haves

Experience applying GenAI techniques within BI and Tableau ecosystems to augment reporting, enable natural-language exploration, and enhance user experience.
Basic Python skills for data manipulation, analytics automation, and integration with Tableau workflows.
Experience with Tableau Prep or similar data preparation tools.
Working knowledge of AWS services (e.g., Glue, Redshift, IAM) and modern data platforms.
Exposure to pharmaceutical, life sciences, or regulated data environments.
Experience collaborating with cross-functional and geographically distributed teams, including external vendors.
Tableau Desktop and/or Server certifications (Developer or Administrator).
Strong communication skills with the ability to explain complex technical concepts to non-technical audiences.

Responsibilities

Data Architecture & Modeling
Design, build, and maintain analytical data models in Amazon Redshift and Snowflake to support enterprise-grade BI and advanced analytics use cases.
Define and implement business transformation logic, conformed dimensions, fact tables, and semantic layers optimized for Tableau performance and scalability.
Partner with data engineering and upstream teams to ensure data quality, consistency, and governance across sources.
Advanced Tableau Development & Visualization
Architect and develop high-quality Tableau dashboards and data products that go beyond standard reporting, incorporating best practices in UX, performance optimization, and storytelling.
Build and maintain Tableau extracts and live connections, ensuring reliability, refresh efficiency, and security compliance.
Act as the technical authority for Tableau on prem and cloud development standards, data source design, and visualization best practices.
Stakeholder Partnership & Delivery
Lead requirements discovery sessions with stakeholders to translate business questions into scalable analytical solutions.
Serve as the primary point of contact for complex analytical questions, ad-hoc requests, and interpretation of data.
Balance short-term business needs with long-term architectural integrity and platform strategy.
Platform Ownership & Modernization
Manage and evolve the Redshift and Snowflake BI data layers, ensuring best practices around performance, security, and cost efficiency.
Continuously evaluate and adopt new BI, analytics, and data-warehouse tools as the platform modernizes.
Mentor and guide other BI resources by modeling strong engineering discipline and analytical thinking.