Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level generalist data analytics role in Bangalore with broad skillset requirements increases applicant competition.
Analytics engineering and BI skills are fairly transferable across industries, though RAG and governance expertise adds specialization.
Explicit 5+ years plus mandatory BI, Databricks, Fabric, and analytics engineering skills create high shortlisting rigidity.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and maintain governed semantic models, reusable business metrics, and curated analytical datasets to support reporting, self-service analytics, and AI use cases.
Partner with Data Engineering, Data Governance, Data Science, and business teams to translate business and analytical requirements into scalable, trusted data products and consumption layers.
Develop and support AI-ready data structures, feature datasets, deployment pipelines, and operational controls for machine learning and generative AI workloads.
Minimum Requirements
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Analytics, Data Science, Mathematics, Statistics, or related technical field.
Minimum 5 years of professional experience in analytics engineering, business intelligence, semantic modeling, data engineering, or related discipline.
Advanced SQL skills including complex query development, optimization, data profiling, and reconciliation at enterprise scale.
Experience with Power BI, Microsoft Fabric, Databricks, Spark, Python, or similar modern data and analytics platforms.
Ideal Candidate Profile
Highly skilled senior individual contributor with deep expertise in semantic modeling, dimensional modeling, and analytics engineering supporting business intelligence and AI.
Experience working with AI concepts like generative AI, retrieval-augmented generation, embeddings, and knowledge graph or ontology-aligned data structures.
Proven ability to collaborate cross-functionally translating complex business definitions and AI requirements into operational, scalable data products and deployment pipelines.