Senior Data Analytics & Automation Engineer

Work Schedule

Environmental Conditions

Job Description

Thermo Fisher Scientific Inc. is the world leader in serving science, with annual revenue exceeding $43 billion. Our Mission is to enable our customers to make the world healthier, cleaner and safer. Whether our customers are accelerating life sciences research, solving complex analytical challenges, improving patient diagnostics and therapies or increasing productivity in their laboratories, we are here to support them. Our global team of more than 125,000 colleagues delivers an unrivaled combination of innovative technologies, purchasing convenience and pharmaceutical services through our industry-leading brands, including Thermo Scientific, Applied Biosystems, Invitrogen, Fisher Scientific, Unity Lab Services and Patheon.

Position Summary:

Join our dynamic EMEA Commercial Analytics team, supporting a multi-division group with revenues exceeding $5BN. As a Sr Engineer, Analytics & Automation, you will be at the forefront of data science and intelligence projects, responsible for driving data-driven insights and leading complex initiatives. Your role will be pivotal in designing and building tools aligned with our commercial  and demand generation strategies.

Key Goals & Responsibilities:

Data Integration and Automation:

• Leverage your expertise to build robust automation processes for seamless data integration.

• Utilize Apache Airflow to create and manage complex data workflows, ensuring efficient and reliable data processing and integration tasks.

• Take advantage of MS Power Automate to streamline data-related processes and automate data flows between various applications and systems.

• Collaborate with cross-functional teams to design, implement, and maintain data integration solutions, ensuring data consistency and accuracy across different platforms.

Database Solutions and Customized Queries:

• Apply your strong SQL skills to develop customized SQL queries for efficient database solutions and fulfill business ad hoc requests. Your experience with Oracle, Cognos, and Redshift will be valuable in this context.

Data Architecture and Data Mart Design:

• Integrate multiple and diverse data sources into a customized data mart, designing an efficient data architecture that can cater to the growing business demands. Leverage AWS data warehousing solutions like Amazon Redshift for scalable data storage and analysis.

Data Modeling:

• Demonstrate a sound understanding of dimensional modeling, star, snowflake schemas, and relationship modeling, ensuring that data models are optimized for performance and usability.

Data Science Projects:

• Lead data science projects independently, from problem definition and data collection to analysis, modeling, and implementation of predictive solutions.

• Utilize Python and related libraries for data analysis, data manipulation, and advanced predictive modeling to drive valuable insights for the commercial teams.

How will these be achieved? Including day to day activities

Business Insights and Solutions:

• Meet with sales leaders, teams, and subject matter experts regularly to understand business opportunities and challenges.

• Convert acquired knowledge into scalable and robust data solutions that enable data-driven decision-making.

Project Leadership and Management:

• Lead and manage complex projects from ideation to implementation, ensuring timely delivery of high-quality data solutions.

• Collaborate with cross-functional teams to define project scopes, objectives, and deliverables.

Data Analysis and Pattern Recognition:

• Utilize a wide variety of internal and external data sources to perform data analysis and identify patterns, trends, and customer behavior insights.

• Apply advanced data analysis and pattern recognition methods to extract actionable insights for business growth.

Data Consolidation and Automation:

• Initiate and lead the process of data consolidation and automation to optimize data workflows and enhance data accessibility across the organization.

Cross-Functional Collaboration:

• Collaborate closely with IT, finance, pricing, marketing, data science, and other functional teams to align analytics goals and achieve defined business objectives.

Metrics Evaluation and Technology Adoption:

• Continually assess the relevance of metrics against business needs and evolving priorities to ensure data-driven decision-making.

• Explore and adopt new relevant technologies and tools to improve data analytics capabilities and efficiency.

Execution of PPI Methodologies:

• Actively seek and execute PPI (Process and Productivity Improvement) methodologies to enhance data processes, quality, and overall business performance.

Requirements/Qualifications:

• Passionate Data Enthusiast: A genuine and unwavering passion for the world of data engineering, analytics, and data science.

• Proficiency in Python (Airflow, Pandas, NumPy) is essential for both data science and data engineering tasks. TensorFlow, SciPy, PySpark, Matplotlib, Seaborn experience is a significant plus.

• Data Modeling Expertise - A strong understanding of dimensional modeling, star, snowflake schemas, and relationship structures, ensuring efficient and optimized data models.

• Proficiency using SQL for querying and manipulating databases, including join operations, nested queries, and data transformation.

• Exposure to CRM systems, especially Salesforce.com, with the ability to extract and analyze data from these systems to support commercial operations.

• Proficiency in using IBM Cognos Analytics, particularly Query Studio, to create and manage data queries.

• Experience building reports and visualizations with Power BI (Power Query and DAX).

• Project Management - Demonstrated ability to lead and manage complex data engineering and analytics projects from inception to completion.

• Innovation and Creativity - Willingness to explore innovative solutions and creative approaches to leverage data for competitive advantages.

• Bachelor's Degree in Computer Science, Mathematics, Statistics, Economics, or a related field is a plus, providing a strong foundation for data-driven problem-solving.

Compensation