Senior Business Intelligence Analyst
• Design and maintain scalable data transformation pipelines and semantic data models.
• Own end-to-end development of BI dashboards and self-service analytics solutions.
• Drive data platform optimization and technical debt reduction.
Woolworths Group India is partnering exclusively with Zinnov Group to recruit for this position.
About the role
As a Senior Business Intelligence Analyst, you are the technical custodian of the platform’s data layers & the front end builds. Embedded within the Data Foundations team, you will architect clean, scalable data transformation pipelines, enforce rigorous data governance, and systematically reduce technical debt. You will champion modern data engineering practices (such as dimensional modeling, dbt package management, and defensive coding) to deliver highly optimised, ‘single-source-of-truth’ data products.
Along with data assets build, require 4+ years of experience in owning & building of visualisations and self-service analytics (preferable in Microstrategy or Looker, Tableau etc.).
What you’ll do
• Develop highly optimized, scalable data transformation pipelines within Google Cloud Platform (BigQuery), utilizing advanced dbt practices and Python to handle enterprise-scale datasets efficiently
• Lead the technical decomposition and reverse-engineering of legacy schema objects, metrics, and attributes from existing platforms, systematically translating them into optimized, version-controlled dbt models and uplifting the semantic layers
• Independently design robust dimensional schemas (Kimball, star schemas) based on complex commercial requirements, ensuring conformed dimensions are standardized across distributed product squads
• Review peer dbt pull requests and LookML changes, enforcing strict code formatting, data modeling frameworks, performance guidelines, and semantic naming consistency to scale overall platform efficiency
• Apply rigorous troubleshooting frameworks to systematically isolate system failures, distinguishing cleanly between platform bugs, raw source data gaps, or transformation logic errors. Account for complex temporal edge cases to guard downstream data reliability
• Proactively partner with data engineering, platform, and analytics / data science teams to align upstream data pipeline ingestion, perform technical quality assurance, and unblock cross-functional platform dependencies
• Regularly experiment with technical proof-of-concepts (POCs) on emerging cloud utilities, keeping the squad abreast of developments in machine learning, AI-first data utilities, and natural language data querying capabilities
• Actively support the professional onboarding and technical growth of mid-level engineers, sharing optimized frameworks, query designs, and automation workflows to elevate overall chapter capability
What you’ll bring
• Strong proficiency in advanced SQL performance tuning, partitioning, and query optimization for handling enterprise-scale datasets inside Google BigQuery
• Strong hands-on background writing modular, production-grade dbt code, managing dbt environments, and orchestrating deployment pipelines
• Extensive experience architecting and managing centralized semantic layers using LookML (projects, explores, joins, and liquid syntax) and optimising downstream front end tool assets
• Solid expertise in data warehousing principles, complex dimensional modeling, star schemas, and building shared conformed dimensions across different business domains
• Demonstrated experience in or exposure to enterprise BI architectures (ideally MicroStrategy, Tableau, LookML or Lookerstudio) and translating their semantic structures into code-driven frameworks. Ability to build, deliver and maintain the Frontend assets
• Able to communicate technical findings in a clear, non-technical way to a variety of stakeholders.
• Advanced troubleshooting skills to systematically isolate environment failures, distinguishing clearly between core data warehouse logic errors, raw source system data bugs, or visualization caching issues
• Proven track record of directly partnering with Data Engineering, Core Platform, and Analytics & Insights / Data Science chapters to manage upstream ingestion schemas and unblock technical pipeline dependencies
• Solid understanding of automated testing frameworks, pull request/code review management, version control guardrails, and continuous integration workflows
• Demonstrated capability to onboard, guide, and share technical knowledge with junior or mid-level team members to lift overall chapter standards
• Practical experience uti