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)

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

Business Intelligence Analyst & Data Engineer- Remote

Job Description