Senior Analytics Engineer

Neutrino Advisory is seeking a Senior Analytics Engineer to build scalable, trusted data foundations that support business decision-making. The role designs and maintains data transformation and semantic layers, establishes analytics engineering practices, and enables governed self-service analytics and AI-powered insights.

Responsibilities:

• Design, develop, and maintain dbt models on Microsoft Fabric, transforming raw source data into clean, well-documented staging, intermediate, and mart layers using consistent modeling standards and naming conventions
• Design and co-own Power BI semantic models, including certified datasets, shared measures, and row-level security, ensuring consistent metric definitions across reports, dashboards, and self-service analytics
• Establish and implement data quality standards through dbt tests, source freshness checks, data contracts, monitoring, and alerting mechanisms
• Implement software engineering best practices for analytics development, including Git-based workflows, pull request reviews, automated CI/CD for dbt and Fabric deployments, and environment management across development, testing, and production
• Collaborate with business stakeholders, finance, operations, and technology teams to translate business requirements into reliable data models, metrics, and analytical solutions
• Establish clear documentation for data models, metrics, business definitions, and data lineage to enable analysts, business users, and AI-powered tools to discover and confidently use enterprise data
• Develop and maintain a catalog of certified datasets, business metrics, and data ownership
• Optimize data transformation pipelines and semantic models for performance, scalability, and cost through incremental loading, partitioning, and efficient query design
• Apply appropriate data access controls, privacy protections, security standards, and auditability requirements in collaboration with information security and compliance teams
• Review technical deliverables, mentor junior analysts and engineers, and promote best practices in analytics engineering and data modeling
• Contribute to the development of reusable templates, technical standards, frameworks, and onboarding documentation
• Evaluate emerging tools, technologies, and architectural approaches to continuously improve the data and analytics ecosystem
• Troubleshoot complex data and pipeline issues, conduct root cause analysis, and communicate resolutions and business impact effectively to technical and non-technical stakeholders

Requirements:

• Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, Information Systems, or a related field, or equivalent practical experience
• 6+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence development, with demonstrated experience delivering production-grade data models
• Advanced SQL skills, including query optimization, dimensional modeling, fact and dimension tables, and slowly changing dimensions (SCD)
• Hands-on experience with dbt in production environments, including modular model development, testing, documentation, macros, incremental models, and deployment practices
• Experience with Microsoft Fabric, including Lakehouse, Warehouse, OneLake, Data Factory, and data pipelines, or comparable cloud data platforms such as Snowflake, Databricks, or BigQuery
• Strong Power BI experience, including semantic model design, DAX, row-level security, and governance of shared datasets
• Familiarity with AI-enabled analytics tools and the data architecture, governed metrics, metadata, and documentation required to support reliable AI-driven insights
• Working knowledge of Git, pull request workflows, and CI/CD pipelines using tools such as Azure DevOps or GitHub Actions
• Proficiency in Python for scripting, automation, and data validation
• Experience implementing data quality, testing, monitoring, and observability practices
• Ability to translate complex or ambiguous business requirements into clear technical designs and communicate architectural decisions and trade-offs effectively
• Strong written and verbal communication skills, with the ability to collaborate across technical and business teams
• Understanding of data privacy, security, governance, and compliance principles
• Experience in financial services, wealth management, banking, healthcare, or other regulated industries
• Familiarity with CRM, portfolio accounting, financial reporting, or custodial data
• Experience building enterprise-scale analytics platforms using Microsoft Azure and Microsoft Fabric
• Exposure to modern data architecture patterns, including medallion architecture, data lakes, lakehouses, and enterprise data warehouses
• Experience working with AI-powered analytics, natural language querying, and enterprise data governance for AI adoption