Business Analyst III

About the Role

As a Product Owner on the Automation, AI and Data (AAD) team, you will play a key role in strengthening data engineering capabilities by delivering data engineering pipelines and solutions through the Enterprise Data Platform (EDP) for various groups and divisions within Thermo Fisher Scientific.

Key Responsibilities

• Product Ownership & Roadmap Management
• Own and prioritize the Data Engineering product backlog based on business value, risk, dependencies, and technical feasibility.
• Define product objectives, roadmap, milestones, acceptance criteria, and measures of success.
• 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.
• Stakeholder & Cross-Functional Management
• 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.

Requirements & Tech Stack

• 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.

• Good to have skills:

• 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.

Qualifications

• Job Title: Business Analyst III
• Work Schedule: Standard (Mon-Fri)
• Environmental Conditions: Office
• Job Location: Bangalore, India