Data Engineering Analytics Lead
Thermo Fisher Scientific1 month ago
Quezon City, PhilippinesStaff+
Responsibilities
- Lead data engineering and analytics infrastructure implementation, including ingestion, APIs, pipelines, transformations, Microsoft Fabric Lakehouse development, storage, orchestration, AI/MCP integrations, technical QA, performance, and maintenance.
- Provide technical architecture guidance covering data modeling, integration, source-to-destination design, and technical standards.
- Develop and improve connected analytics systems, tools, automations, integrations, and Smartsheet-based resources.
- Implement data modeling, transformation, ETL/ELT workflows, Power BI measures and queries, and data integrity controls using Power Query, SQL, Python/PySpark, and related methods.
- Implement or support AI-enabled analytics foundations, API integrations, orchestration, structured outputs, AI-ready datasets, custom GPTs, evaluation workflows, and tool-calling frameworks.
- Perform statistical and non-statistical analysis, identify performance drivers, and contribute analysis and visualization to dashboards, reports, presentations, and other analytics products.
- Investigate analytics and operational challenges and lead or support solution implementation and maintenance.
- Coordinate dependencies, sequencing, handoffs, communication, knowledge sharing, and escalation across Manila-based team members.
- Support best-practice documentation, standard operating procedures, and instructional materials.
Requirements
- Bachelor’s degree in marketing, data science, psychology, business, communications, or a related field.
- At least 4 years of relevant experience in data management, analytics engineering, analysis, and visualization.
- Strong proven Power BI experience, including DAX and M queries for visualization.
- Hands-on experience with data modeling in Power BI and Microsoft Fabric Lakehouse environments.
- Experience applying data engineering patterns and best practices and using at least one orchestration tool such as Azure Data Factory, Power Automate, or Databricks.
- Strong experience building data pipelines and using Power Query, Spark, Python, R, and SQL for data integration and analysis.
- Experience with AI APIs, GPT-based workflows, or related integration patterns; experience with Flowise, server-side orchestration, structured outputs, evaluation workflows, logging, or MCP is strongly preferred.
- Experience deriving insights from large datasets through statistical hypothesis testing and descriptive, diagnostic, predictive, or prescriptive analysis.
- Some experience building data capture solutions with platforms such as Smartsheet, Airtable, Zoho, Adobe, Google, Matomo, Sprinklr, or Sprout.
- Experience coordinating multidisciplinary work, managing handoffs and dependencies, communicating effectively, and escalating issues appropriately.
- Strong strategic thinking and consideration of the downstream impact of data management decisions.
Benefits
- Second-shift afternoon work schedule.
- Office-based work environment.
- Opportunity to support global communications analytics, AI-enabled workflows, and data-driven initiatives at Thermo Fisher Scientific.
Tech Stack
Categories
Data Engineering
About Thermo Fisher Scientific
Thermo Fisher Scientific builds analytical instruments, lab equipment and consumables, specialty diagnostics, and provides pharma services (CDMO and clinical research) for biotech, pharma, academic, and healthcare customers. It earns revenue through product sales, e-commerce distribution (Fisher Scientific), and contract services under brands such as Thermo Scientific, Applied Biosystems, Invitrogen, Patheon, and PPD. Headquartered in Waltham, MA, it is publicly traded on the NYSE (TMO) and was created by the 2006 merger of Thermo Electron and Fisher Scientific.