Sr. Manager, Analytics Engineering & BI Architecture

Merck•Boston, MA

1d•Hybrid

About The Position

At EMD Serono, data, analytics, and AI are core to how we bring innovative medicines to patients faster. As a Senior Data & MLOps Engineer, you own the data and MLOps foundations - from ingestion to production - collaborating with Data Science, Commercial, Medical, Analytics, and IT to turn prototypes into scalable, governed solutions.

Requirements

Bachelor’s degree in computer science, Engineering, or a related technical field, or equivalent work experience.
4+ years of experience in data engineering, MLOps, machine learning engineering, software engineering, cloud engineering, or related technical roles.
Hands-on experience building production data pipelines, ETL/ELT workflows, derived tables, curated datasets, or feature-ready data assets.
Experience deploying or operationalizing machine learning models, advanced analytics workflows, or AI-enabled solutions.
Advanced SQL, Python, and shell scripting.
Proven track record of architecting, deploying, and maintaining complex projects in a diverse ecosystem of AWS services.
Experience with AWS automation through CloudFormation and Azure DevOps.
Experience with Git, CI/CD, containerization, and production software engineering practices, including troubleshooting build failures.
Familiarity with model monitoring, model versioning, experiment tracking, testing, and deployment automation.
Experience with designing proactive alerting and monitoring workflows for workloads running on AWS services.
Strong communicator who works independently and collaborates effectively across technical and business stakeholders.

Nice To Haves

7+ years of experience across data engineering, MLOps, ML engineering, analytics engineering, or software engineering.
Experience in pharmaceutical, biotechnology, healthcare, or life sciences environments and datasets such as claims, EMR/EHR, patient services, customer engagement, field activity, marketing, or sales data.
Experience with Snowflake, dbt, Airflow, IICS or equivalent ETL/orchestration tools, parquet files, and large-scale data processing patterns.
Experience with AWS ML/data services, MLflow, SageMaker, Kubeflow, Docker, Kubernetes, or Terraform.
Experience implementing data quality checks, data observability, lineage, pipeline monitoring, model monitoring, drift detection, batch inference, real-time inference, or automated retraining.
Hands-on experience with GenAI applications (RAG, vector databases, LLM evaluation).
AWS certifications - AWS Certified DevOps Engineer Professional; AWS Certified Data Engineer
Experience with Tableau, Power BI, or other BI tools for data visualization and reporting.

Responsibilities

Build, maintain, and troubleshoot scalable data pipelines, ETL/ELT workflows, derived tables, curated datasets, and feature-ready data assets using SQL and Python.
Deploy and operationalize machine learning, advanced analytics, and generative AI solutions through automated deployment, monitoring, and operational workflows.
Enable GenAI use cases involving embeddings, vector databases, retrieval-augmented generation, prompt management, and evaluation workflows.
Establish reusable engineering patterns, CI/CD practices, testing frameworks, documentation, and governance standards for data and ML solutions.
Apply data privacy, security, quality, lineage, access control, and compliance standards appropriate for healthcare and pharmaceutical data.