BI Tech Lead at Thermo Fisher… · Lagunilla

Work Schedule Standard (Mon-Fri)

Environmental Conditions Office

Job Description

Position Summary

The Lead Data Engineer – Supply Chain Analytics is responsible for building and delivering scalable data pipelines, curated datasets, and analytics solutions that enable end-to-end visibility across supply chain operations.

This role combines strong hands-on data engineering skills with deep supply chain domain expertise, supporting key functions such as demand planning, inventory management, procurement, manufacturing, and logistics. The position focuses on developing reliable data assets, enabling analytics, and improving supply chain performance through data-driven insights.

Key Responsibilities

Data Engineering & Pipeline Development

• Design, build, and maintain robust ETL/ELT pipelines integrating data from ERP, CRM, and other supply chain systems.
• Develop and optimize data ingestion, transformation, and processing workflows using cloud-based platforms (AWS, Databricks, Redshift).
• Ensure data quality, consistency, and reliability across critical supply chain datasets.
• Implement best practices for data validation, monitoring, and pipeline performance optimization.

Supply Chain Data & Analytics Enablement

• Partner with Supply Chain teams to deliver data solutions supporting planning, inventory, procurement, and logistics operations.
• Build and maintain curated datasets and data models aligned with key supply chain KPIs (forecast accuracy, inventory planning, service levels).
• Enable timely and accurate reporting and analytics for operational and strategic decision-making.
• Support scenario analysis and performance tracking across supply chain functions.

Advanced Analytics & Data Products

• Prepare and manage datasets for forecasting, optimization, and other advanced analytics use cases.
• Support deployment of machine learning models within data pipelines and production environments.
• Enable near real-time data availability for critical operational use cases.
• Contribute to development of data products that improve supply chain visibility and responsiveness.

Data Quality & Governance

• Apply and enforce data quality checks, data governance standards, and master data practices.
• Ensure alignment with data security and compliance requirements.
• Maintain data documentation, lineage, and metadata for supply chain datasets.

Collaboration & Delivery

• Work closely with Supply Chain, Analytics, and IT teams to translate business needs into data solutions.
• Support delivery of dashboards and reporting solutions using BI tools such as Power BI.
• Collaborate with data scientists and analysts to ensure data readiness and usability.
• Contribute to continuous improvement of data engineering practices and workflows.

Qualifications

Education & Experience

• Bachelor’s degree in Computer Science, Engineering, Supply Chain, or related field; advanced degree preferred.
• 10–12+ years of experience in Data Engineering, BI, or Analytics, with supply chain exposure.
• Proven experience delivering data pipelines and analytics solutions for supply chain or operations domains.

Skills & Knowledge

• Strong hands-on expertise in data engineering (ETL/ELT, data pipelines, data modeling).
• Experience with cloud data platforms (AWS, Databricks, Redshift or similar).
• Proficiency in SQL, Python, and distributed processing frameworks (e.g., Spark).
• Experience working with ERP and supply chain systems (SAP, etc.).
• Strong understanding of supply chain processes (planning, inventory, procurement, logistics).
• Experience building datasets and data models for supply chain KPIs and reporting.
• Familiarity with BI tools (Power BI).
• Knowledge of data quality, governance, and master data management concepts.
• Experience supporting advanced analytics or ML use cases is a plus.
• Exposure to real-time data processing or streaming frameworks is a plus.

Strong problem-solving and collaboration skills.