Analytics Engineer (Position located in Bengaluru, India)

Responsibilities:

• Build and maintain the dbt transformation layer (staging → intermediate → marts) with dimensional modeling, tests, and documentation, delivering clean, analysis-ready datasets for reporting and analytics.
• Partner with business stakeholders to translate reporting and analytics requirements into well-modeled, certified data definitions and BI-ready datasets.
• Design, build, and maintain Snowflake Semantic Views — defining the tables, relationships, facts, dimensions, and metrics that form the governed, reusable definitions layer on top of the warehouse.
• Author and curate verified queries, synonyms, and metric definitions so semantic views reliably power natural-language and AI-driven querying (e.g., Cortex Analyst / MCP-based access).
• Use AI/LLM tools (e.g., Claude, Copilot, or similar) as part of the daily engineering workflow — for code generation, query drafting, documentation, and accelerating model development.
• Own data quality, testing, and documentation for your models, and support the governance and certification of datasets and semantic views for broad self-service use.
• Enable BI and downstream consumers (Looker/Tableau) by exposing consistent, trustworthy metrics through the modeled and semantic layers.

Skills:

• Hands-on production experience building analytics models with dbt (staging/marts patterns, tests, documentation).
• Strong dimensional and semantic data modeling skills — star schemas, facts vs. dimensions, grain, and slowly changing dimensions.
• Production experience with Snowflake, including building or maintaining semantic views / semantic models (or a comparable semantic/metrics layer such as LookML, dbt Semantic Layer/MetricFlow, or Cube).
• Ability to translate business metric definitions into governed, reusable data models and semantic objects.
• Git-based version control and code review workflows for analytics code.
• Strong SQL skills and hands-on Python experience for data transformation, testing, and tooling.
• Practical, working knowledge of LLMs/generative AI — how they're prompted, their capabilities and limitations — and comfort using AI coding/analytics assistants as everyday tools.