Analytics Engineer

WarriorBabe is seeking an Analytics Engineer who can own the reliability and performance of our Supabase/PostgreSQL database and turn business data into clear, actionable insights.

You will maintain data integrations and refresh processes, develop reliable data models, and build useful Metabase dashboards. You will also work directly with leadership and business teams to investigate performance, explain what is driving results, identify opportunities, and recommend measurable actions.

You will help evaluate and improve our data architecture as the business evolves. We welcome recommendations grounded in business needs, reliability, performance, maintainability, and cost.

Success means keeping the data accurate, current, and well documented—and using it to help the business make better decisions.

• Manage and improve our Supabase/PostgreSQL reporting database, including tables, views, materialized views, indexes, and SQL queries.
• Diagnose slow queries, blocking, failed refreshes, and resource bottlenecks; use evidence to prioritize improvements.
• Maintain appropriate permissions, review schema changes, and support backup and recovery readiness.
• Plan and validate database changes with clear dependency checks, migration steps, and rollback procedures.
• Evaluate the existing architecture and recommend practical improvements while preserving reporting continuity.

• Build, maintain, and troubleshoot production n8n workflows used for data ingestion, synchronization, and reporting.
• Maintain Fivetran connections and custom API integrations, with clear ownership of each source, writer, destination, and downstream dependency.
• Work with CRM, ERM, and other business systems as required.
• Handle authentication, pagination, rate limits, duplicate events, partial failures, retries, and safe backfills.
• Monitor actual data delivery and freshness, including failures that may be hidden behind a successful workflow or scheduler status.

• Develop reusable SQL models with clear record definitions, keys, relationships, and treatment of historical changes.
• Maintain agreed definitions for revenue, clients, conversion, refunds, renewals, retention, and other business metrics.
• Reconcile reporting with its underlying sources and distinguish missing data from a verified zero.
• Prevent duplicate counts and inconsistent totals across dashboards, including differences caused by dates, time zones, attribution, or counting rules.
• Document data lineage, metric definitions, refresh behavior, known limitations, and operating procedures.

• Build and maintain Metabase dashboards, saved questions, filters, and record-level drill-throughs.
• Translate stakeholder questions into reporting that supports a specific business decision.
• Verify saved configuration and the dashboard users actually see, including filters, totals, drill-throughs, and links to operational records.
• Maintain documentation alongside analytical dashboards and preserve dependent reports when making changes.

• Proactively analyze acquisition, sales conversion, revenue, refunds, renewals, retention, and coaching performance.
• Investigate performance changes by relevant cohorts, products, channels, teams, and time periods.
• Distinguish business changes from data-quality problems and clearly separate evidence, hypotheses, and remaining uncertainty.
• Present findings with their business impact, recommended action, and a practical way to measure the result.
• Partner with business owners to evaluate whether actions taken improved outcomes.

• Use AI tools such as Claude or OpenAI to accelerate SQL development, data modeling, n8n workflow design, troubleshooting, documentation, and business analysis.
• Use AI to explore patterns, develop hypotheses, review queries and calculations, and identify potential errors or edge cases.
• Validate AI-generated code, calculations, and conclusions against source data and reproducible checks before deploying changes or presenting findings.
• Protect confidential information and protected health information (PHI) when using AI, following company policies and using approved tools.

• Hands-on ownership of a production PostgreSQL or Supabase database, including troubleshooting and performance optimization.
• Strong SQL skills, including joins, CTEs, window functions, aggregation, query plans, and indexing.
• Practical experience building and troubleshooting production n8n workflows. Be prepared to explain a workflow you personally implemented and how it handled failures.
• Experience with API integrations, webhooks, authentication, pagination, rate limits, and reliable synchronization.
• Experience with Metabase or a comparable BI platform, with the ability to work hands-on in Metabase.
• Demonstrated ability to investigate business performance, quantify findings, and recommend actions.
• Practical experience using AI tools such as Claude or OpenAI to build, improve, analyze, and check work, with the ability to explain and independently verify the results.
• Ability to explain technical issues and business conclusions clearly to nontechnical stakeholders.
• Experience using Git and maintaining clear technical documentation and controlled production changes.

• Working knowledge of HIPAA and the privacy and security considerations involved in handling protected health information (PHI) across databases, integrations, and reporting.
• CRM and ERM data structures, relationships, and integrations.
• Fivetran configuration and monitoring.
• Payment, subscription, refund, renewal, or customer-retention reporting.
• Python or JavaScript for scripting and automation.
• Experience with Metabase for understanding and reconciling existing reports.

• Business teams can understand what key metrics mean and trace results to supporting records.
• Data integrations and reporting refreshes operate within agreed freshness and completeness expectations, with actionable monitoring.
• Database and dashboard improvements have documented validation and preserve dependent reporting.
• Leadership receives useful explanations and recommendations, with follow-up on the actions taken.
• Architecture recommendations include their benefits, tradeoffs, costs, and a practical implementation path.