Data Modeller at Unilever

, , India -

Full Time

Start Date

Immediate

Expiry Date

26 Mar, 26

Salary

0.0

Posted On

26 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Modeling, Data Engineering, Data Architecture, Data Governance, SQL, NoSQL, Data Warehousing, Data Lakes, Business Process Redesign, Data Integration, Entity Relationship Diagrams, Data Validation, Performance Optimization, Collaboration, Mentorship, Documentation

Industry

Manufacturing

Description

Job Title: Data Modeller Location: Bangalore, India ABOUT UNILEVER: Be part of the world’s most successful, purpose-led business. Work with brands that are well-loved around the world, that improve the lives of our consumers and the communities around us. We promote innovation, big and small, to make our business win and grow; and we believe in business as a force for good. Unleash your curiosity, challenge ideas and disrupt processes; use your energy to make this happen. Our brilliant business leaders and colleagues provide mentorship and inspiration, so you can be at your best. Every day, nine out of ten Indian households use our products to feel good, look good and get more out of life – giving us a unique opportunity to build a brighter future. Every individual here can bring their purpose to life through their work. Join us and you’ll be surrounded by inspiring leaders and supportive peers. Among them, you’ll channel your purpose, bring fresh ideas to the table, and simply be you. As you work to make a real impact on the business and the world, we’ll work to help you become a better you Role Overview: Unilever is seeking an experienced Data Modeller Lead for the Data & Analytics domain. This position is ideal for candidates with approximately 8–12 years of experience in data modeling and engineering. In the context of the transformation, the Data Modeller Lead will serve as a functional data modeling expert at the intersection of business process redesign and technical data architecture. The role’s primary purpose is to lead the design and implementation of a harmonized global data model that aligns with standardized processes and systems upgrade (e.g., transitioning from SAP ECC to modern ERP and data platforms). The Data Modeller Lead will bridge Unilever’s current-state data landscape (including legacy SAP ECC and SAP BW schemas, and the existing Data Lake/Data Foundation environment) with the future-state, harmonized data model being developed by Global Process Owners (GPOs) program. They will ensure that business requirements and KPIs are translated into robust data structures, and that the new data model integrates seamlessly across both legacy and new systems. The role emphasizes functional alignment – understanding and translating business needs into data design – over pure technical coding, while still requiring strong technical acumen in data modeling. Ultimately, this position is about ensuring Unilever’s next-generation data models are fit-for-purpose, scalable, and well-governed, enabling the business to leverage high-quality data for analytics and decision-making as we execute the program. Key Responsibilities: Enterprise Data Model Design: Lead the development of the conceptual, logical, and physical data models for Unilever’s harmonized Data & Analytics environment. This includes designing data structures for data lakes/warehouses and data marts that capture business-critical information across domains (Procure-to-Pay, Finance, Supply Chain, etc.), in line with standardized process requirements. Ensure that data modeling follows best practices and supports both relational (SQL) and non-relational (NoSQL) paradigms as needed. Current-to-Future Data Mapping: Serve as the point person for mapping existing data objects and schemas from current systems (SAP ECC, legacy data warehouses) to the new data model. Identify gaps, divergences, or mismatches between the current state and future design, and develop solutions or transformations to address these. Ensure continuity of key business metrics and KPIs by guaranteeing they are properly accommodated in the new model. Functional Specifications & Documentation: Translate business requirements and process changes into clear functional data specifications. Document & manage translation mapping on how legacy data (e.g., fields in ECC tables) correspond to new data entities and attributes in the harmonized model across the data foundation layers. Generate detailed and end to end entity relationship between existing and new schema. Prepare detailed data definitions, entity-relationship diagrams, and mapping documents that will guide data engineers in building out the new data layer within Unilever’s Data Foundation platform. Generate scalable data model design with right partition and other data modelling strategies to ensure that the design is built for Unilever scale. Collaboration with Process & Business Teams: Work closely with Global Process Owners (GPOs) and functional experts (Finance, Supply Chain, Procurement, etc.) to co-create the harmonized data model. Act as a functional interface between business teams and technical teams: ensure that the data model supports standardized business processes being implemented, and conversely, educate business stakeholders on how data structures will change. This includes active participation in design workshops and process blueprint sessions, representing the data perspective and ensuring business needs are met through data design. Data Model Implementation & Testing Support: Partner with data engineering teams and IT implementation teams to bring the new data model to life. Oversee and guide the build of data pipelines and integration flows from source systems (new ERP, legacy ECC, etc.) into the target data lake/warehouse based on the model. Develop and execute test plans or data validation strategies to verify that data is correctly migrated/transformed according to the specifications. Support system integration testing (SIT) and user acceptance testing (UAT) by resolving any data-related issues, ensuring the new structures produce accurate and expected results in analytics and reporting outputs. Data Architecture & Performance Optimization: Ensure that the expansion of Unilever’s data architecture under adheres to best practices for performance and scalability. This involves setting data modeling standards and guidelines (naming conventions, normalization rules, etc.), and reviewing designs to prevent performance bottlenecks. Where needed, perform hands-on modeling and optimization tasks – for example, optimizing query performance in the data lake or designing aggregate structures for faster retrieval. Continually assess and govern the data model as it evolves, making sure it remains sustainable and efficient as new data is onboarded. Business Enablement & Data Utilization: Once the new harmonized data layer is in place, support business and analytics teams in leveraging it. Provide guidance or training sessions on how to use the new data model, explaining changes in data definitions or structures to end-users (e.g., how a particular KPI is now calculated or where to find certain data in the new schema). Work with product owners of analytics tools (like PowerBI dashboards, etc.) to ensure they can easily transition their solutions to use the new data model. Testing & Data Reconciliation: Take an active role in system testing and user acceptance testing (UAT) phases. Define “golden data sets” (authoritative sample data) for testing purposes and use them to validate that the new data model outputs correct results. Design Coordination: Work closely with solution design and application teams to incorporate data requirements into the overall system design. Ensure that any changes in business process or application design are reflected in the data model design and vice versa. Manage design documentation related to data (for example, data definitions, design decisions, or architecture diagrams) and keep it updated throughout the project. Coordinate approvals for data design changes by liaising with architects and relevant stakeholders so that the implementation aligns with Unilever’s standards. Data Governance and Quality: Champion data governance practices throughout the modeling work. Define clear ownership of data elements in the new model, maintain data dictionaries, and ensure compliance with Unilever’s data quality standards and security/privacy policies. Implement controls so that data in the new model remains consistent (e.g., master data alignment across systems) and establish processes for ongoing maintenance of the model (e.g., handling new fields or changes in a governed manner). Team Leadership & Coaching: (De-emphasized technical management but included for completeness) Lead and mentor a small team of data modelers or analysts if assigned. While this role is primarily an individual contributor in functional design, it may involve providing guidance to junior modelers/engineers working on data projects. Foster best practices in the team through knowledge sharing, create training content on modern data modeling concepts for Data SMEs, and cultivate a mindset of continuous improvement and collaboration. At HUL, we believe that every individual irrespective of their race, colour, religion, gender, sexual orientation, gender identity or expression, age, nationality, caste, disability or marital status can bring their purpose to life. So apply to us, to unleash your curiosity, challenge ideas and disrupt processes; use your energy to make the world a better place. As you work to make a real impact on the business and the world, we’ll work to help you become a better you! "All official offers from Unilever are issued only via our Applicant Tracking System (ATS). Offers from individuals or unofficial sources may be fraudulent—please verify before proceeding." Better Business. Better World. Better You. We’ve been pioneers, innovators and future-makers for over 120 years. Learn More

Responsibilities

Lead the design and implementation of a harmonized global data model that aligns with standardized processes and systems upgrades. Collaborate with Global Process Owners and functional experts to ensure that business requirements are translated into robust data structures.