We are looking for Senior Business and Data Consultant

Company Overview

At Datavid, we are on a journey to build the most flexible and scalable, data-centric Knowledge Engine. Our platform seamlessly integrates underlying data sources through the use of our proprietary web and API applications. We are looking for experienced and passionate engineers to build these interfaces and work with our clients, which will give our clients easy, intuitive, and fine-grained access to the Knowledge Engine.

This is an opportunity to join a world-class team of collaborative and passionate software engineers.

Role Overview

The Data / Business Analyst plays a key role in Customer’s Data Hub initiative, supporting the delivery of standardized, trusted, and compliant regulatory data across the organization. This role act as the bridge between business requirements, data governance, and technical implementation ensuring that data pipelines, models, and mappings are accurate, validated, and ready for business use.

Key Responsibilities

·     Collaborate with source system owners and regulatory stakeholders to gather and refine data pipeline requirements.

·     Translate data pipeline requirements into epics and stories ready for refinement in Jira.

·     Lead backlog refinement sessions with data architects, domain leads, and scientists to prioritize data ingestion and transformation activities.

·     Define story structure to comprise the requirements of the work to be done and the definition of done and the different types of tasks relevant for the domain to accomplish with their associated roles, documented in a Confluence page.

·     Endorse the role of agile scrum master for the RDH team: coach team members in self-management, organize high-value increments in sprints meeting the definition of done

·     Identify and document potential data issues including quality, normalization, duplication, completeness, or governance gaps and share findings with the Data Domain and platform Lead.

·     Work closely with Data Architects to define and document the data transformation pipeline architecture.

·     Support the Data Architect in defining and maintaining the data models used across regulatory use cases by reviewing proposed changes and evaluate their relevance towards new requirements.

·     Support the review phase led by the Data Domain Lead, verifying that the regulatory and business context is preserved across transformed datasets.

·     Validate that all data mappings and transformations meet regulatory compliance, data quality, and analytical readiness standards.

·     Collaborate with Data Scientists to define mapping logic for regulatory entities such as substances, studies, and documents.

·     Review the design of quality control pipelines, ensuring mapping accuracy and alignment with analytical expectations.

Key Deliverables

·     Comprehensive and well-documented data pipeline and transformation specifications

·     Defined and validated data models and business-aligned mapping definitions

·     Clear and traceable backlog refinement inputs and prioritization documentation

·     Continuous collaboration and feedback loop with the Data Domain Lead, Platform Lead and Data Science teams

Skills and Qualifications

·     Bachelor’s degree in data analytics, computer science

·     5+ years of experience in data or business analysis e.g., Life Sciences, Agrochemical, or Pharma domain

·     Demonstrated expertise in data transformation pipelines, and data quality frameworks

·     Familiarity with metadata management and data governance principles

·     Strong analytical skills, with the ability to translate business requirements into technical deliverables

·     Excellent communication and documentation skills, with experience collaborating across disciplines e.g. Data Scientists, Architects, Domain Leads, Platform Leads

·     A collaborative and proactive approach, comfortable working in iterative, agile environments

·     High attention to detail with strong problem-solving capabilities

·     Self-motivated and adaptable, able to navigate ambiguity and evolving project priorities in a complex data environment