Business Analyst III

Data Analysis and Quality

• Use SQL to analyze datasets, investigate data-quality issues, validate engineering outputs, and support root-cause analysis.
• Define and validate controls for completeness, consistency, reconciliation, duplicates, exceptions, outliers, and business rules.
• Establish exception-management processes, remediation workflows, and accountability for master-data changes.

Data Engineering Collaboration

• Collaborate with Data Engineers on pipelines, curated datasets, data-quality solutions, and analytical data products.
• Participate in discussions involving Databricks, PySpark, SQL, transformations, source-to-target mappings, data models, and pipeline dependencies.

Domain and Delivery Alignment

• Align engineering priorities with Supply Chain processes including demand planning, inventory planning, procurement, product data, and ERP operations.
• Coordinate business or UAT validation, verify deliverables before release, and monitor adoption, data quality, and outcomes after implementation.

Required Skills and Experience

• Strong SQL skills covering complex queries, reconciliation, exception identification, and data-quality analysis.
• Solid understanding of ETL/ELT, pipelines, transformations, data models, warehouses or lakehouses, mappings, and data-quality frameworks.
• Working knowledge of Databricks and PySpark notebooks or code.
• Product Owner or Agile experience with backlogs, user stories, acceptance criteria, prioritization, and engineering collaboration.
• Strong stakeholder communication and requirement management across business and technical groups.

Good-to-Have Skills

• Snowflake or Azure data engineering experience is beneficial.
• Knowledge of PostgreSQL, MySQL, MongoDB, or Cassandra is beneficial.
• ERP and master data experience, Power BI or Tableau, cloud data engineering certifications, and basic machine learning or Generative AI knowledge are beneficial.

About Thermo Fisher Scientific