BI Analytics Engineer

Role Summary

We are seeking an experienced BI Analytics Engineer to design, develop, and manage enterprise-grade analytics solutions with a strong focus on Snowflake Semantic Views, governed data modeling, Tableau enablement, and self-service business intelligence.

The ideal candidate combines strong functional understanding with analytics engineering expertise and can translate complex business data into trusted, scalable semantic models. These models will provide consistent metrics and business definitions for business users, analysts, Tableau dashboards, AI solutions, and other analytical consumers.

Key Responsibilities

Snowflake Semantic Layer Development:

·         Design, build, and maintain Snowflake Semantic Views and curated data products.

·         Develop reusable, business-friendly semantic models that standardize metrics, KPIs, dimensions, hierarchies, and calculations.

·         Establish governed data assets that provide a single source of truth for reporting, analytics, and AI use cases.

Analytics Engineering:

·         Translate business requirements into scalable dimensional models, star schemas, and subject-area data marts.

·         Build curated analytical layers optimized for Tableau, self-service analytics, and AI consumption.

·         Define business metrics, relationships, and reusable calculation logic.

Data Engineering and Integration:

·         Develop and maintain ETL/ELT pipelines feeding Snowflake analytical environments.

·         Integrate data from operational systems, SaaS platforms, APIs, and third-party sources.

·         Implement automated data-quality, validation, monitoring, lineage, and reconciliation controls.

·         Support batch and near-real-time analytical pipelines.

Tableau and Reporting Enablement:

·         Partner with stakeholders to understand reporting and analytical needs.

·         Enable Tableau through performant, governed semantic models and certified data sources.

·         Improve reporting consistency, scalability, and self-service adoption.

AI and Data Product Enablement:

·         Build AI-ready datasets and semantic context for GenAI and analytical applications.

·         Collaborate with Data Science and AI teams to prepare trusted analytical and feature datasets.

Governance and Best Practices:

·         Define standards for semantic modeling, metadata, lineage, cataloging, security, and privacy.

·         Document architectural decisions, metric definitions, and business rules.

·         Promote modern DataOps and analytics-engineering practices.

Required Skills & Experience

·         5–7 years of experience in BI Engineering, Analytics Engineering, Data Engineering, or Data Warehousing.

·         3+ years of hands-on Snowflake experience.

·         Demonstrated experience designing enterprise analytical and semantic models.

·         Experience enabling executive reporting, operational dashboards, and self-service analytics through Tableau.

Mandatory Skills

·         Snowflake Semantic Views and semantic-layer architecture

·         Snowflake data modeling and performance optimization

·         Dimensional modeling, Kimball methodology, star schemas, and data marts

·         Metric-layer development and governed KPI definitions

·         Advanced SQL and analytical transformation design

·         ETL/ELT development and data-quality frameworks

·         Tableau data-source design, dashboard enablement, and performance optimization

·         Python; PySpark preferred

·         Snowflake Tasks, Streams, secure data sharing, and role-aware data access

Functional / Domain Experience

·         Experience in at least one of the following domains:

·         Finance: FP&A, revenue analysis, budgeting, forecasting, or P&L analytics

·         Sales: sales operations, pipeline analytics, CRM insights, or revenue growth

·         Operations: process optimization, workforce planning, productivity, or operational analytics

Technical Skills

·         Snowflake Data Cloud architecture and modeling

·         Snowpark using Python or SQL

·         Streams, Tasks, Dynamic Tables, and Snowpipe

·         Structured, semi-structured, and unstructured data management

·         Query tuning, workload optimization, and cost management

·         Tableau semantic consumption and certified data sources

·         AI-ready data modeling, feature-store concepts, and RAG foundations

·         Vector search, semantic data models, and Snowflake Cortex AI capabilities