Data Engineering Analytics Lead

Job description

Work Schedule Second Shift (Afternoons) Environmental Conditions Office Job Description As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer. DESCRIPTION: As Analytics Lead – Data Engineering , your focus will be on leading data engineering and the technical implementation and maintenance of analytics infrastructure (e.g. data storage, collection tools, data pipelines, and AI-enabled analytics foundations), building and executing data management processes (e.g. modeling, transformation, and AI-ready data preparation), and applying analytical approaches to performance data (e.g. hypothesis testing and identification of performance drivers). You will also advise the Communications Analytics, Automation, and AI Program Manager on analytics architecture decisions, providing recommendations and translating agreed architectural direction into scalable technical solutions. In addition, you will support the development of AI-enabled analytics workflows, including API-based data integration and emerging analytics use cases. You will also serve as a local coordination point for the Manila-based team, helping coordinate execution across team members, including dependencies, sequencing, and handoffs; supporting team communication and knowledge sharing; and surfacing issues or decisions requiring escalation. Your technical expertise across these areas will help accelerate execution of scalable, reliable analytics capabilities that support decision-making and advance key initiatives within the Communications analytics program. This unique and exciting role will enable you to collaboratively pioneer innovative data-driven approaches within the broader Global Communications function, supporting analytics goals, stakeholder needs, and the continued growth of a vibrant, data-driven culture. KEY RESPONSIBILITIES Data Engineering and Analytics Infrastructure: Lead data engineering and technical infrastructure implementation across the analytics ecosystem, including ingestion, APIs, pipelines, transformations, Microsoft Fabric Lakehouse development, storage, orchestration, AI/MCP integrations, technical QA, performance, maintenance, and reusable datasets and shared semantic infrastructure. Apply appropriate engineering and architectural best practices to deliver scalable, reliable, maintainable solutions. Provide technical guidance and recommendations to the Communications Analytics Program Manager on architecture decisions, including data modeling, integration, source-to-destination design, and technical standards. Analytics Systems and Workflow Infrastructure: Lead or contribute to technical development and continuous improvement of connected analytics systems, tools, automations, and integrations, including Smartsheet-based resources, with a focus on scalability, reusability, maintainability, and how components connect and evolve over time. Data Management and ETL (Extract, Transform, Load) Workflows: Lead data modeling, transformation, and ETL/ELT implementation in alignment with agreed architectural direction , using Power Query, SQL, Python/PySpark and other appropriate query- or notebook-based transformation methods; developing PBI measures and queries, and ensuring data integrity across sources. AI Platform and Analytics Workflow Implementation: Lead or support, as appropriate, the technical implementation of AI-enabled analytics foundations and workflows, including API-based integrations, AI/MCP technical design and implementation, tool or service orchestration, structured outputs, AI-ready datasets, custom GPTs, technical QA and evaluation, and other technical components that improve analysis efficiency, expand access to insights, and advance conversational and other AI-enabled analytics use cases. Analysis and Visualization: Perform statistical and non-statistical analyses to support defined analytical questions and hypotheses, identify patterns and performance drivers, and contribute technical analysis and visualization to dashboards, reports, presentations, and other analytics products. Partner with the Communications Analytics Program Manager and other analytics colleagues on interpretation, insight development, and analytical storytelling. Problem Solving: Investigate analytics and operational challenges, identify effective solutions, and lead or support their implementation and ongoing maintenance. Local Team Coordination: Help coordinate execution across Manila-based team members, including dependencies, sequencing, and handoffs; support effective communication and knowledge sharing across roles; and surface risks, resource needs, or decisions requiring escalation. Best Practice Documentation: Support development of best practices, standard operating procedures, and instructional materials to promote analytics success, support a growing data culture, and foster innovation. QUALIFICATIONS AND SKILLS Power BI Expertise: Strong, proven experience with Power BI, including building DAX and M queries to drive visualization. Data Modeling and Orchestration: Strong, hands-on experience with data modeling in Power BI and Microsoft Fabric Lakehouse environments, including experience evaluating and applying appropriate data engineering patterns and best practices, as well as experience with at least one orchestration tool (e.g. Azure Data Factory, Power Automate, Databricks). ETL Processes: Strong experience building data pipelines, extracting and transforming data via Power Query, and utilizing data integration and analysis languages such as SPARK, Python, R, SQL. AI Platform, Integration, and Automation Experience: Experience using or supporting AI APIs, GPT-based workflows, or related integration patterns to improve analytics, reporting, insight generation, or workflow efficiency. Experience with AI workflow orchestration platforms (e.g. Flowise), server-side orchestration, structured outputs, evaluation workflows, logging, or tool-calling frameworks such as MCP is strongly preferred. Data Analysis: Experience deriving insights from large datasets (e.g. statistical hypothesis tests, descriptive, diagnostic, predictive, prescriptive analysis) and translating them into recommendations. Data Capture Solutions Design: Some experience building data collection solutions within various platforms, including data capture (e.g. Smartsheet, AirTable, Zoho), web analytics (e.g. Adobe, Google, Matomo), and social analytics (e.g. Sprinklr, Sprout) tools. Team Coordination: Experience coordinating work across multidisciplinary team members, supporting effective handoffs and communication, resolving day-to-day dependencies, and escalating issues appropriately. Strategic thinking : Strong experience considering the long-term, downstream impact of data management activities to ensure actions support immediate and future success and mitigate future challenges. Education and Field Experience: Bachelor’s degree in marketing, data science, psychology, business, communications, or related field. 4 + years of relevant experience required, with demonstrated hands-on experience in data management, analytics engineering, analysis, and visualization. Note: This job description is intended to outline the general responsibilities and qualifications of the position. It is not an exhaustive list of all duties, responsibilities, and skills required. Other duties may be assigned as needed.