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ThermoFisher Scientific Inc. is the world leader in serving science, with revenues of more than $40 billion and approximately 1,20,000 employees globally. We help our customers accelerate life sciences research, solve complex analytical challenges, improve patient diagnostics, deliver medicines to market and increase laboratory productivity.
About Team : We are Automation, AI and Data (AAD) team that caters to data engineering and analytics, automation and AI solutions for various groups and divisions within Thermofisher Scientific.
As a product owner, you will play a key role in strengthening our data engineering capabilities delivering data engineering pipelines and solutions through Enterprise Data Platform (EDP) for various groups and divisions.
Own and prioritize the Data Engineering product backlog based on business value, risk, dependencies, and technical feasibility.
Business Requirement Translation
Partner with Supply Chain, Finance, Data Owners, Data Stewards, SMEs, IT teams, and other stakeholders to understand business problems.
Translate business requirements and data-quality rules into clear user stories, technical requirements, data rules, and acceptance criteria for Data Engineers.
Data Analysis & Validation
Perform hands-on SQL analysis to understand datasets, investigate data-quality issues, validate engineering outputs, and support root-cause analysis.
Define and validate data-quality controls covering completeness, consistency, business rules, reconciliation, duplicates, exceptions, and outliers . These are particularly relevant to the supplied UOM audit procedures.
Data Engineering Collaboration
Work closely with Data Engineers to design and deliver reliable data pipelines, curated datasets, data-quality solutions, and analytical data products.
Participate in discussions involving Databricks, PySpark, SQL, data transformations, source-to-target mappings, data models, and pipeline dependencies .
Act as the primary bridge between business stakeholders and technical teams.
Facilitate requirement workshops, backlog refinement, sprint planning, demos, issue resolution, and stakeholder updates.
Communicate technical concepts, limitations, risks, and trade-offs in business-friendly language.
Data Quality & Governance
Establish data-quality requirements, validation rules, exception-management processes, and remediation workflows.
Collaborate with Data Owners and Data Stewards to establish accountability and appropriate review/approval processes for master-data changes.
Business Domain & Value Alignment
Understand how data supports Supply Chain processes such as demand planning, inventory planning, procurement, product/master data and ERP operations , as well as related Finance measures.
Ensure engineering priorities are connected to measurable outcomes such as inventory accuracy, planning accuracy, operational efficiency, data-quality improvement, and cost savings.
Testing, Release & Adoption
Define acceptance criteria and coordinate business/UAT validation of engineering deliverables.
Validate that delivered datasets and data products meet functional requirements before release and monitor adoption, data quality, and business outcomes after implementation.

Must have skills and experience
Strong SQL skills – ability to write complex queries involving joins, CTEs, aggregations, window functions, reconciliation, exception identification, and data-quality analysis.
Data Engineering understanding – solid understanding of ETL/ELT, data pipelines, data transformations, data models, data warehouses/lakehouses, source-to-target mappings, and data-quality frameworks.
Databricks & PySpark familiarity – working knowledge of the Databricks environment and ability to understand PySpark notebooks/code and discuss transformation logic with Data Engineers. Deep software-engineering expertise is not required.
Product Owner / Agile experience – demonstrated ability to manage product backlogs, write user stories and acceptance criteria, prioritize requirements, and work effectively with engineering teams in Agile delivery models.
Stakeholder communication & requirement management – strong ability to work across technical teams, business SMEs, leadership, Data Owners and Data Stewards and translate between business and technical terminology.
Hands on experience with Snowflake or Azure data engineering.
Knowledge of SQL and NoSQL databases like PostgreSQL, MySQL, MongoDB Cassandra.
ERP and Master Data experience
Data visualization experience using tools such as Power BI or Tableau.
Relevant certifications in data engineering on cloud platforms.
Basic understanding of machine learning and Generative AI concepts.

As one team of 1,20,000 colleagues, we share a common set of values - Integrity, Intensity, Innovation and Involvement - working together to accelerate research, solve complex scientific challenges, drive technological innovation and support patients in need.
ThermoFisher Scientific is an EEO/Affirmative Action Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status.

Business Analyst - SQL/ Power Bi

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