We're looking for a hybrid Business Intelligence Analyst & Data Engineer who can operate across the full data lifecycle — from building and maintaining reliable data pipelines to designing dashboards and reports that drive business decisions. This role sits at the intersection of data infrastructure and analytics: you'll own how data flows into our systems and how it's ultimately consumed by stakeholders across the organization.

This is a great fit for someone who enjoys both the technical rigor of data engineering and the storytelling/analytical side of BI work, and who wants ownership over an end-to-end data product rather than sitting in just one lane. AI-driven analytics is a growing part of our roadmap, so we're especially interested in candidates who are curious about applying AI/LLM tools to make data more accessible and self-service across the business.

Key Responsibilities

Data Engineering

• Design, build, and maintain ETL/ELT pipelines that integrate data from multiple source systems (CRM, ERP, project management tools, spreadsheets, APIs) into centralized data warehouses or data marts
• Ensure data quality, consistency, and integrity across pipelines through validation, testing, and monitoring
• Optimize data models (star/snowflake schemas) for reporting performance
• Automate manual data processes and reduce reliance on ad hoc spreadsheet work
• Manage data source connections, credentials, and refresh schedules
• Troubleshoot and resolve data pipeline failures, latency issues, and discrepancies

Business Intelligence & Analytics

• Build and maintain dashboards and reports (e.g., in Tableau, Power BI, or similar) that translate raw data into clear, actionable insights
• Partner with business stakeholders to gather reporting requirements and translate them into technical specifications
• Define and maintain a single source of truth for key business metrics (a data dictionary / metrics glossary)
• Conduct ad hoc analysis to answer business questions and support strategic decision-making
• Monitor dashboard usage and proactively improve reports based on stakeholder feedback
• Document data lineage, business logic, and calculation methodology for transparency and auditability

AI-Enabled Analytics

• Design and maintain data structures (clean schemas, semantic layers, metric definitions) that are well-suited for consumption by AI agents and natural-language query tools, not just human-facing dashboards
• Build or support AI-powered reporting assistants/chatbots that let stakeholders ask business questions in plain language and get accurate, governed answers
• Evaluate and integrate LLM-based tools into the analytics stack (e.g., automated insight generation, anomaly detection, natural-language-to-SQL interfaces)
• Establish guardrails for AI-generated analysis — certified data sources, citation of underlying data, and validation processes to ensure trustworthy outputs
• Stay current on AI/BI tooling trends and proactively recommend where automation or AI can reduce manual reporting work

Cross-Functional Collaboration

• Work closely with department leaders (Sales, Finance, Operations, Professional Services, etc.) to understand data needs and pain points
• Act as a technical liaison between business teams and IT/engineering on data-related initiatives
• Support the rollout of new tools, data sources, or automation initiatives (e.g., AI-powered reporting agents, workflow automation platforms)

Requirements

• Bachelor's degree in Computer Science, Data Science, Information Systems, Statistics, or a related field (or equivalent practical experience)
• 3+ years of experience in a data engineering, BI analyst, or analytics engineering role
• Strong SQL skills, including experience writing complex queries, optimizing performance, and working with large datasets
• Experience building and maintaining ETL/ELT pipelines (custom scripts, or tools like Fivetran, dbt, Airflow, Workato, or similar)
• Hands-on experience with a BI/visualization tool (Tableau, Power BI, Looker, or similar)
• Solid understanding of data modeling concepts (dimensional modeling, normalization, star schemas)
• Experience working with cloud data warehouses (Snowflake, BigQuery, Redshift, or similar)
• Strong analytical mindset with the ability to translate ambiguous business questions into structured data analysis
• Excellent communication skills — able to explain technical concepts to non-technical stakeholders

Preferred Qualifications

• Experience with Python or another scripting language for data manipulation and automation
• Familiarity with CRM/ERP systems (Salesforce, NetSuite) and how operational data flows from them
• Experience with API integrations and workflow automation platforms (Workato, Zapier, or similar)
• Hands-on experience building or integrating AI/LLM-powered tools (e.g., Claude, ChatGPT, Copilot) into data workflows — such as natural-language querying, automated report generation, or AI-driven data agents
• Familiarity with prompt engineering, retrieval-augmented generation (RAG), or agent-based architectures as applied to analytics
• Experience establishing data governance practices (certified data sources, metric definitions, documentation standards) suitable for both human and AI consumers
• Prior experience in a Professional Services, Finance, or Operations analytics function