📍 Job Overview

Job Title: Associate Specialist, Scientific Data Cloud Engineering
Company: MSD
Location: IND - Telangana - Hyderabad (HITEC City), India
Job Type: Full-Time, Hybrid
Category: Data Engineering, Cloud Engineering
Date Posted: June 25, 2025
Experience Level: Mid-Senior Level (2-5 years)

🚀 Role Summary

Lead a team of data engineers in Hyderabad to support multiple data products for the Scientific Data Consumption Product Line within the Research and Development Sciences Value Team.
Manage various aspects of data engineering and IT infrastructure, focusing on high-performance computing and IT service management.
Collaborate with cross-functional teams to ensure data products meet business requirements and user needs.

📝 Enhancement Note: This role involves a mix of technical leadership, team management, and stakeholder communication, requiring a strong background in data engineering and IT infrastructure management.

💻 Primary Responsibilities

Team Leadership & Management: Establish and lead a team of data engineers, fostering a culture of collaboration, innovation, and continuous learning.
Data Engineering: Design, develop, and maintain data pipelines, databases, and data processing systems to support scientific data consumption products.
IT Infrastructure Management: Oversee IT infrastructure, ensuring high availability, scalability, and performance of data systems.
Stakeholder Communication: Work closely with research and development teams, understanding their data needs and translating them into technical requirements.
Project Management: Plan, execute, and deliver data engineering projects within defined timelines and budgets.
Problem Solving: Troubleshoot and resolve complex data engineering and IT infrastructure issues.

📝 Enhancement Note: This role requires a balance of technical depth and breadth, with a strong focus on problem-solving, project management, and stakeholder communication.

🎓 Skills & Qualifications

Education: Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. Relevant certifications (e.g., AWS, Azure, Google Cloud) are a plus.

Experience: 2-5 years of experience in data engineering, cloud engineering, or a related role. Proven experience in leading teams and managing projects.

Required Skills:

Data engineering and management
Cloud computing (AWS, Azure, or Google Cloud)
IT infrastructure management
Software development and system administration
IT service management (ITSM)
High-performance computing (HPC)
Incident management and capacity planning
Change controls and release management
Solution architecture and system design

Preferred Skills:

Experience with scientific data or life sciences domain
Familiarity with big data technologies (e.g., Hadoop, Spark, Hive)
Knowledge of data governance and data quality principles
Agile project management methodologies
Experience with containerization (e.g., Docker, Kubernetes) and orchestration (e.g., ECS, AKS, GKE)

📝 Enhancement Note: Candidates should have a strong foundation in data engineering, cloud computing, and IT infrastructure management, with a preference for those with experience in scientific data or life sciences domains.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio showcasing previous data engineering projects, highlighting your technical skills and problem-solving abilities.
Case studies demonstrating your experience in data pipeline design, data processing, and data management.
Examples of your leadership and team management skills, such as project charters, team structures, and project outcomes.

Technical Documentation:

Well-commented code and documentation demonstrating your attention to detail and commitment to code quality.
Project documentation, including data flow diagrams, system architecture overviews, and deployment processes.
Test cases and performance metrics, showcasing your approach to quality assurance and optimization.

📝 Enhancement Note: Given the leadership and team management aspects of this role, candidates should focus on demonstrating their ability to lead projects, manage teams, and communicate effectively in their portfolio.

💵 Compensation & Benefits

Salary Range: INR 12,00,000 - 18,00,000 per annum (Estimated based on market research and role complexity)

Benefits:

Health, dental, and vision insurance
Retirement savings plans
Employee stock purchase plan
Tuition assistance and professional development opportunities
Employee assistance programs and wellness resources

Working Hours: 40 hours per week, with flexible work arrangements and remote work options available.

📝 Enhancement Note: The estimated salary range is based on market research and role complexity, with adjustments for regional cost of living and experience level.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceuticals and Life Sciences

Company Size: Large (50,000+ employees)

Founded: 1892

Team Structure:

The team will consist of data engineers reporting directly to the Associate Specialist.
The team will collaborate with cross-functional teams, including research and development, data science, and IT infrastructure teams.

Development Methodology:

Agile and iterative development methodologies will be employed to manage data engineering projects.
Regular code reviews, testing, and quality assurance practices will be followed to ensure high-quality data products.
Continuous integration and continuous deployment (CI/CD) pipelines will be used to automate deployment and ensure consistent system performance.

Company Website: www.msd.com

📝 Enhancement Note: MSD is a large, established pharmaceutical company with a strong focus on research and development. This role will involve working closely with cross-functional teams to deliver data products that support scientific research and innovation.

📈 Career & Growth Analysis

Web Technology Career Level: Mid-Senior Level (2-5 years)

Reporting Structure: This role reports directly to the Manager, Scientific Data Consumption Product Line within the Research and Development Sciences Value Team.

Technical Impact: This role will have a significant impact on the delivery of scientific data consumption products, ensuring they meet business requirements and user needs. The role will also influence the design and maintenance of data engineering and IT infrastructure systems.

Growth Opportunities:

Technical Growth: Expand your expertise in data engineering, cloud computing, and IT infrastructure management. Explore emerging technologies and trends in data engineering and scientific data consumption.
Leadership Growth: Develop your leadership and team management skills, with opportunities to mentor junior team members and take on more significant projects.
Career Progression: With experience and proven success in this role, you may progress to senior data engineering roles, data architecture roles, or management roles within the data engineering or IT infrastructure domains.

📝 Enhancement Note: This role offers significant growth opportunities in both technical expertise and leadership skills, with a clear path for career progression within the data engineering and IT infrastructure domains.

🌐 Work Environment

Office Type: Hybrid (combination of on-site and remote work)

Office Location(s): IND - Telangana - Hyderabad (HITEC City), India

Workspace Context:

Collaboration: The hybrid work environment encourages collaboration and knowledge sharing among team members and with cross-functional teams.
Work Tools: Modern workspaces equipped with necessary tools, including multiple monitors and testing devices, to support data engineering and IT infrastructure management tasks.
Team Interaction: Regular team meetings and one-on-one sessions to foster open communication and continuous learning.

Work Schedule: Flexible work arrangements, with core working hours between 9:00 AM and 5:00 PM IST. Some flexibility is available for deployment windows, maintenance, and project deadlines.

📝 Enhancement Note: The hybrid work environment at MSD fosters collaboration and knowledge sharing, with flexible work arrangements to support a healthy work-life balance.

📄 Application & Technical Interview Process

Interview Process:

Technical Screening: A phone or video call to assess your technical skills and problem-solving abilities in data engineering and IT infrastructure management.
Cultural Fit Interview: A conversation with the hiring manager to evaluate your cultural fit within the team and organization.
Final Interview: A discussion with senior leadership to assess your leadership potential and strategic thinking.

Portfolio Review Tips:

Highlight your leadership and team management skills, showcasing your ability to lead data engineering projects and manage teams.
Demonstrate your problem-solving abilities and technical expertise in data engineering and IT infrastructure management.
Provide case studies and examples that illustrate your experience with high-performance computing, IT service management, and data governance principles.

Technical Challenge Preparation:

Brush up on your data engineering and IT infrastructure management skills, focusing on high-performance computing, incident management, and capacity planning.
Familiarize yourself with MSD's products and services, understanding their data consumption needs and how your role can support them.

ATS Keywords: (Organized by category)

Data Engineering: Data pipelines, data processing, data management, big data technologies, data governance, data quality
Cloud Computing: AWS, Azure, Google Cloud, cloud architecture, cloud migration, cloud security
IT Infrastructure Management: IT service management (ITSM), incident management, capacity planning, change controls, release management, system administration
Leadership & Team Management: Team leadership, project management, stakeholder communication, mentoring, coaching
Problem-Solving: Troubleshooting, root cause analysis, problem-solving methodologies, decision-making frameworks
Project Management: Agile methodologies, project planning, project execution, project delivery, project management tools

📝 Enhancement Note: The interview process at MSD focuses on assessing your technical skills, problem-solving abilities, and cultural fit within the organization. Be prepared to demonstrate your leadership and team management skills, as well as your technical expertise in data engineering and IT infrastructure management.

🛠 Technology Stack & Web Infrastructure

Data Engineering Technologies:

Big data technologies: Hadoop, Spark, Hive, Pig, Impala
Cloud computing platforms: AWS, Azure, Google Cloud
Data processing and transformation tools: Apache Beam, Apache Flink, Apache Kafka, Apache NiFi
Data warehousing and data management platforms: Amazon Redshift, Google BigQuery, Azure Synapse Analytics, Snowflake
Data governance and data quality tools: Talend, Informatica, Pentaho, Apache Atlas

IT Infrastructure Management Tools:

IT service management (ITSM) tools: ServiceNow, BMC Remedy, Jira Service Management
Monitoring and logging tools: Prometheus, Grafana, ELK Stack, Datadog, New Relic
Infrastructure as Code (IaC) tools: Terraform, CloudFormation, Azure Resource Manager, Google Cloud Deployment Manager
Containerization and orchestration tools: Docker, Kubernetes, ECS, AKS, GKE

Collaboration and Productivity Tools:

Collaboration platforms: Microsoft 365, Google Workspace, Slack, Microsoft Teams
Project management tools: Jira, Asana, Trello, Microsoft Project
Version control systems: Git, GitHub, GitLab, Bitbucket

📝 Enhancement Note: MSD uses a mix of open-source and commercial tools for data engineering and IT infrastructure management. Familiarize yourself with these technologies and be prepared to discuss your experience with them in the interview process.

👥 Team Culture & Values

Data Engineering Values:

Data-Driven Decision Making: Emphasize the importance of data-driven decision-making in supporting scientific research and innovation.
Collaboration: Foster a culture of collaboration and knowledge sharing among data engineering teams and with cross-functional teams.
Continuous Learning: Encourage continuous learning and professional development in data engineering and emerging technologies.
Quality and Excellence: Strive for high-quality data products and systems, ensuring they meet business requirements and user needs.

Collaboration Style:

Cross-Functional Integration: Work closely with research and development teams, data science teams, and IT infrastructure teams to ensure data products meet business requirements and user needs.
Code Review Culture: Encourage regular code reviews and pair programming to ensure high-quality data products and systems.
Knowledge Sharing: Foster a culture of knowledge sharing and mentoring, with regular team meetings and one-on-one sessions to support continuous learning and professional development.

📝 Enhancement Note: MSD's data engineering teams value data-driven decision-making, collaboration, continuous learning, and quality and excellence. Candidates should be prepared to discuss their experience with these values and how they have applied them in previous roles.

⚡ Challenges & Growth Opportunities

Technical Challenges:

High-Performance Computing: Design and maintain high-performance computing systems to support scientific data consumption products.
Data Governance and Data Quality: Ensure data governance principles are followed and maintain high data quality standards in data engineering systems.
IT Infrastructure Management: Manage IT infrastructure systems, ensuring high availability, scalability, and performance of data systems.
Emerging Technologies: Stay up-to-date with emerging technologies in data engineering and scientific data consumption, and explore their application in MSD's products and services.

Learning & Development Opportunities:

Technical Skill Development: Expand your expertise in data engineering, cloud computing, and IT infrastructure management through training, workshops, and online courses.
Conference Attendance: Attend industry conferences and events to learn from thought leaders and network with other professionals in the data engineering and scientific data consumption domains.
Mentorship and Leadership Development: Seek mentorship opportunities and develop your leadership skills through team management, project management, and stakeholder communication experiences.

📝 Enhancement Note: This role presents significant technical challenges and learning opportunities, with a strong focus on high-performance computing, data governance, and IT infrastructure management. Candidates should be prepared to discuss their experience with these challenges and how they have overcome them in previous roles.

💡 Interview Preparation

Technical Questions:

Data Engineering: Describe your experience with data pipeline design, data processing, and data management. Discuss the challenges you've faced and how you've overcome them.
Cloud Computing: Explain your experience with cloud computing platforms (AWS, Azure, Google Cloud) and how you've used them to support data engineering projects.
IT Infrastructure Management: Discuss your experience with IT service management (ITSM), incident management, capacity planning, change controls, and release management. Provide examples of how you've used these principles to manage IT infrastructure systems.
Leadership & Team Management: Describe your experience with team leadership, project management, and stakeholder communication. Provide examples of how you've led data engineering projects and managed teams to deliver successful outcomes.

Company & Culture Questions:

MSD's Products and Services: Demonstrate your understanding of MSD's products and services, and how your role can support their data consumption needs.
Data-Driven Decision Making: Explain how you've used data-driven decision-making in previous roles to support scientific research and innovation.
Collaboration and Knowledge Sharing: Describe your experience with cross-functional collaboration and knowledge sharing, and how you've used these principles to deliver successful data engineering projects.

Portfolio Presentation Strategy:

Live Demonstration: Prepare a live demonstration of your data engineering portfolio, showcasing your technical skills and problem-solving abilities.
Case Studies: Develop case studies that illustrate your experience with high-performance computing, data governance, and IT infrastructure management.
Technical Deep Dive: Be prepared to discuss the technical details of your data engineering projects, including data flow diagrams, system architecture overviews, and deployment processes.

📝 Enhancement Note: The interview process at MSD focuses on assessing your technical skills, problem-solving abilities, and cultural fit within the organization. Be prepared to discuss your experience with data engineering, cloud computing, IT infrastructure management, and leadership and team management, as well as your understanding of MSD's products and services and data-driven decision-making principles.

📌 Application Steps

To apply for this Associate Specialist, Scientific Data Cloud Engineering position at MSD:

Customize Your Resume: Tailor your resume to highlight your data engineering, cloud computing, and IT infrastructure management skills, as well as your leadership and team management experience.
Prepare Your Portfolio: Showcase your data engineering projects, highlighting your technical skills, problem-solving abilities, and leadership experience. Include case studies that demonstrate your experience with high-performance computing, data governance, and IT infrastructure management.
Research MSD: Familiarize yourself with MSD's products and services, understanding their data consumption needs and how your role can support them. Be prepared to discuss your understanding of MSD's data engineering and IT infrastructure management practices.
Prepare for Technical Interviews: Brush up on your data engineering, cloud computing, and IT infrastructure management skills, focusing on high-performance computing, incident management, and capacity planning. Practice common data engineering and IT infrastructure management interview questions and prepare your responses.
Prepare for Behavioral Interviews: Reflect on your leadership and team management experience, identifying specific examples of how you've led data engineering projects, managed teams, and communicated with stakeholders. Practice common behavioral interview questions and prepare your responses.
Apply: Submit your application through the MSD careers website, following the instructions provided in the job posting.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and data engineering industry-standard assumptions. All details should be verified directly with MSD before making application decisions.

Associate Specialist, Scientific Data Cloud Engineering